Gastos Casa
01 July, 2022
Gastos_casa %>%
dplyr::select(-Tiempo,-link) %>%
dplyr::select(fecha, gasto, monto, gastador,obs) %>% tail(30) %>%
knitr::kable(.,format = "html", format.args = list(decimal.mark = ",", big.mark = "."),
caption="Tabla 1. Gastos Casa (últimos 30 registros)", align =rep('c', 3)) %>%
kableExtra::kable_styling(bootstrap_options = c("striped", "hover"),font_size = 8) %>%
kableExtra::scroll_box(width = "100%", height = "300px")| fecha | gasto | monto | gastador | obs |
|---|---|---|---|---|
| 12/5/2022 | Comida | 28.790 | Andrés | cafe musetti |
| 10/5/2022 | Enceres | 18.230 | Andrés | Compraste Quinoa Fusión Banquete 400g y 1 producto más |
| 15/5/2022 | Enceres | 13.380 | Tami | Sensodyne |
| 18/5/2022 | Comida | 41.970 | Tami | NA |
| 19/5/2022 | VTR | 21.990 | Andrés | NA |
| 21/5/2022 | Pila estufa | 6.414 | Andrés | Pila cr2 switchbot |
| 25/5/2022 | Reloj | 8.914 | Andrés | Reloj alarma temperatura y humedad |
| 28/5/2022 | Comida | 138.918 | Tami | Wild Foods |
| 29/5/2022 | Parafina | 42.490 | Tami | NA |
| 30/5/2022 | Comida | 50.346 | Tami | NA |
| 30/5/2022 | Netflix | 8.320 | Tami | NA |
| 1/6/2022 | Diosi | 7.000 | Andrés | Pilas collar |
| 3/6/2022 | Electricidad | 24.792 | Andrés | Pac enel 01686518 |
| 6/6/2022 | Enceres | 19.400 | Tami | Caja Papel Higiénico |
| 7/6/2022 | Comida | 15.260 | Andrés | NA |
| 7/6/2022 | Comida | 23.450 | Andrés | NA |
| 13/6/2022 | Comida | 57.775 | Tami | NA |
| 18/6/2022 | Gas | 81.350 | Andrés | NA |
| 19/6/2022 | VTR | 21.990 | Andrés | NA |
| 20/6/2022 | Electricidad | 67.655 | Andrés | NA |
| 21/6/2022 | Comida | 38.000 | Andrés | NA |
| 21/6/2022 | Comida | 15.000 | Andrés | Flor de loto verduras |
| 24/6/2022 | Comida | 40.400 | Andrés | Bar la Providencia |
| 27/6/2022 | Agua | 12.502 | Andrés | PAC AGUAS ANDIN 000000005687837 |
| 29/6/2022 | Netflix | 8.320 | Tami | NA |
| 29/6/2022 | Comida | 68.213 | Tami | NA |
| 30/6/2022 | Comida | 15.310 | Tami | NA |
| 30/6/2022 | Electricidad | 67.655 | Andrés | NA |
| 31/3/2019 | Comida | 9.000 | Andrés | NA |
| 8/9/2019 | Comida | 24.588 | Andrés | Super Lider |
#para ver las diferencias depués de la diosi
Gastos_casa %>%
dplyr::mutate(fecha= lubridate::parse_date_time(fecha, c("%d/%m/%Y"),exact=T)) %>%
dplyr::mutate(fecha=strftime(fecha, format = "%Y-W%V")) %>%
dplyr::mutate(gastador=ifelse(gastador=="Andrés",1,0)) %>%
dplyr::group_by(gastador, fecha,.drop = F) %>%
dplyr::summarise(gasto_media=mean(monto,na.rm=T)) %>%
dplyr::mutate(treat=ifelse(fecha>"2019-W26",1,0)) %>%
#dplyr::mutate(fecha_simp=lubridate::week(fecha)) %>%#después de diosi. Junio 24, 2019
dplyr::mutate(gastador_nombre=plyr::revalue(as.character(gastador), c("0" = "Tami", "1"="Andrés"))) %>%
assign("ts_gastos_casa_week_treat", ., envir = .GlobalEnv)
gplots::plotmeans(gasto_media ~ gastador_nombre, main="Promedio de gasto por gastador", data=ts_gastos_casa_week_treat,ylim=c(0,75000), xlab="", ylab="")
par(mfrow=c(1,2))
gplots::plotmeans(gasto_media ~ gastador_nombre, main="Antes de Diosi", data=ts_gastos_casa_week_treat[ts_gastos_casa_week_treat$treat==0,], xlab="", ylab="", ylim=c(0,70000))
gplots::plotmeans(gasto_media ~ gastador_nombre, main="Después de Diosi", data=ts_gastos_casa_week_treat[ts_gastos_casa_week_treat$treat==1,], xlab="", ylab="",ylim=c(0,70000))
library(ggiraph)
library(scales)
#if( requireNamespace("dplyr", quietly = TRUE)){
gg <- Gastos_casa %>%
dplyr::mutate(fecha= lubridate::parse_date_time(fecha, c("%d/%m/%Y"),exact=T)) %>%
dplyr::mutate(gastador=ifelse(gastador=="Andrés",1,0)) %>%
dplyr::mutate(fecha_simp=tsibble::yearweek(fecha)) %>%
dplyr::mutate(fecha_week=strftime(fecha, format = "%Y-W%V")) %>%
dplyr::mutate(treat=ifelse(fecha_week>"2019 W26",1,0)) %>%
dplyr::mutate(gastador_nombre=plyr::revalue(as.character(gastador), c("0" = "Tami", "1"="Andrés"))) %>%
# dplyr::mutate(week=as.Date(as.character(lubridate::floor_date(fecha, "week"))))%>%
#dplyr::mutate(fecha_week= lubridate::parse_date_time(fecha_week, c("%Y-W%V"),exact=T)) %>%
dplyr::group_by(gastador_nombre, fecha_simp) %>%
summarise(monto_total=sum(monto)) %>%
dplyr::mutate(tooltip= paste0(substr(gastador_nombre,1,1),"=",round(monto_total/1000,2))) %>%
ggplot(aes(hover_css = "fill:none;")) +#, ) +
#stat_summary(geom = "line", fun.y = median, size = 1, alpha=0.5, aes(color="blue")) +
geom_line(aes(x = fecha_simp, y = monto_total, color=as.factor(gastador_nombre)),size=1,alpha=.5) +
ggiraph::geom_point_interactive(aes(x = fecha_simp, y = monto_total, color=as.factor(gastador_nombre),tooltip=tooltip),size = 1) +
#geom_text(aes(x = fech_ing_qrt, y = perc_dup-0.05, label = paste0(n)), vjust = -1,hjust = 0, angle=45, size=3) +
# guides(color = F)+
sjPlot::theme_sjplot2() +
geom_vline(xintercept = as.Date("2019-06-24"),linetype = "dashed") +
labs(y="Gastos (en miles)",x="Semanas y Meses", subtitle="Interlineado, incorporación de la Diosi; Azul= Tami; Rojo= Andrés") + ggtitle( "Figura 4. Gastos por Gastador") +
scale_y_continuous(labels = f <- function(x) paste0(x/1000)) +
scale_color_manual(name = "Gastador", values= c("blue", "red"), labels = c("Tami", "Andrés")) +
scale_x_yearweek(date_breaks = "1 month", minor_breaks = "1 week", labels=scales::date_format("%m/%y")) +
theme(axis.text.x = element_text(vjust = 0.5,angle = 35), legend.position='bottom')+
theme(
panel.border = element_blank(),
panel.grid.major = element_blank(),
panel.grid.minor = element_blank(),
axis.line = element_line(colour = "black")
)
# x <- girafe(ggobj = gg)
# x <- girafe_options(x = x,
# opts_hover(css = "stroke:red;fill:orange") )
# if( interactive() ) print(x)
#}
tooltip_css <- "background-color:gray;color:white;font-style:italic;padding:10px;border-radius:10px 20px 10px 20px;"
#ggiraph(code = {print(gg)}, tooltip_extra_css = tooltip_css, tooltip_opacity = .75 )
x <- girafe(ggobj = gg)
x <- girafe_options(x,
opts_zoom(min = 1, max = 3), opts_hover(css =tooltip_css))xplot<-Gastos_casa %>%
dplyr::mutate(fecha= lubridate::parse_date_time(fecha, c("%d/%m/%Y"),exact=T)) %>%
dplyr::mutate(fecha_week=strftime(fecha, format = "%Y-W%V")) %>%
dplyr::mutate(month=as.Date(as.character(lubridate::floor_date(fecha, "month"))))%>%
dplyr::group_by(month)%>%
summarise(gasto_total=sum(monto)/1000) %>%
ggplot2::ggplot(aes(x = month, y = gasto_total)) +
geom_point()+
geom_line(size=1) +
sjPlot::theme_sjplot2() +
geom_vline(xintercept = as.Date("2019-06-24"),linetype = "dashed") +
geom_vline(xintercept = as.Date("2019-03-23"),linetype = "dashed", color="red") +
labs(y="Gastos (en miles)",x="Meses/Año", subtitle="Interlineado, incorporación de la Diosi") +
ggtitle( "Figura 5. Suma de Gastos por Mes") +
scale_x_date(breaks = "1 month", minor_breaks = "1 month", labels=scales::date_format("%m/%y")) +
theme(axis.text.x = element_text(vjust = 0.5,angle = 45))
plotly::ggplotly(plot) plot2<-Gastos_casa %>%
dplyr::mutate(fecha= lubridate::parse_date_time(fecha, c("%d/%m/%Y"),exact=T)) %>%
dplyr::mutate(fecha_week=strftime(fecha, format = "%Y-W%V")) %>%
dplyr::mutate(day=as.Date(as.character(lubridate::floor_date(fecha, "day"))))%>%
dplyr::group_by(day)%>%
summarise(gasto_total=sum(monto)/1000) %>%
ggplot2::ggplot(aes(x = day, y = gasto_total)) +
geom_line(size=1) +
sjPlot::theme_sjplot2() +
geom_vline(xintercept = as.Date("2019-06-24"),linetype = "dashed") +
geom_vline(xintercept = as.Date("2020-03-23"),linetype = "dashed", color="red") +
labs(y="Gastos (en miles)",x="Meses/Año", subtitle="Interlineado, incorporación de la Diosi") +
ggtitle( "Figura 5. Suma de Gastos por Día") +
scale_x_date(breaks = "1 month", minor_breaks = "1 week", labels=scales::date_format("%m/%y")) +
theme(axis.text.x = element_text(vjust = 0.5,angle = 45))
plotly::ggplotly(plot2) tsData <- Gastos_casa %>%
dplyr::mutate(fecha= lubridate::parse_date_time(fecha, c("%d/%m/%Y"),exact=T)) %>%
dplyr::mutate(fecha_week=strftime(fecha, format = "%Y-W%V")) %>%
dplyr::mutate(day=as.Date(as.character(lubridate::floor_date(fecha, "day"))))%>%
dplyr::group_by(day)%>%
summarise(gasto_total=sum(monto))%>%
dplyr::mutate(covid=case_when(day>as.Date("2019-06-02")~1,TRUE~0))%>%
dplyr::mutate(covid=case_when(day>as.Date("2020-03-10")~covid+1,TRUE~covid))%>%
dplyr::mutate(covid=as.factor(covid))%>%
data.frame()
tsData_gastos <-ts(tsData$gasto_total, frequency=7)
mstsData_gastos <- forecast::msts(Gastos_casa$monto, seasonal.periods=c(7,30))
tsData_gastos = decompose(tsData_gastos)
#plot(tsData_Santiago, title="Descomposición del número de casos confirmados para Santiago")
forecast::autoplot(tsData_gastos, main="Decomposición de los Gastos Diarios")+
theme_bw()+ labs(x="Weeks")
tsdata_gastos_trend<-cbind(tsData,trend=as.vector(tsData_gastos$trend))%>% na.omit()#tsData_gastos$trend
#Using the inputted variables, a Type-2 Sum Squares ANCOVA Lagged Dependent Variable model is fitted which estimates the difference in means between interrupted and non-interrupted time periods, while accounting for the lag of the dependent variable and any further specified covariates.
#Typically such analyses use Auto-regressive Integrated Moving Average (ARIMA) models to handle the serial dependence of the residuals of a linear model, which is estimated either as part of the ARIMA process or through a standard linear regression modeling process [9,17]. All such time series methods enable the effect of the event to be separated from general trends and serial dependencies in time, thereby enabling valid statistical inferences to be made about whether an intervention has had an effect on a time series.
#it uses Type-2 Sum Squares ANCOVA Lagged Dependent Variable model
#ITSA model da cuenta de observaciones autocorrelacionadas e impactos dinámicos mediante una regresión de deltas en rezagados. Una vez que se incorporan en el modelo, se controlan.
#residual autocorrelation assumptions
#TSA allows the model to account for baseline levels and trends present in the data therefore allowing us to attribute significant changes to the interruption
#RDestimate(all~agecell,data=metro_region,cutpoint = 21)
#Ver con CHANGEPOINT
##http://www.lancs.ac.uk/~killick/Pub/KillickEckley2011.pdf
itsa_metro_region_quar<-
its.analysis::itsa.model(time = "day", depvar = "gasto_total",data=tsData,
interrupt_var = "covid",
alpha = 0.05,no.plots = F, bootstrap = TRUE, Reps = 10000, print = F) 
print(itsa_metro_region_quar)## [[1]]
## [1] "ITSA Model Fit"
##
## $aov.result
## Anova Table (Type II tests)
##
## Response: depvar
## Sum Sq Df F value Pr(>F)
## interrupt_var 4.8968e+10 2 19.4334 <2e-16 ***
## lag_depvar 7.7414e+08 1 0.6144 0.4335
## Residuals 5.8459e+11 464
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## $tukey.result
## Tukey multiple comparisons of means
## 95% family-wise confidence level
##
## Fit: stats::aov(formula = x$depvar ~ x$interrupt_var)
##
## $`x$interrupt_var`
## diff lwr upr p adj
## 1-0 8373.715 -7002.807 23750.24 0.4069353
## 2-0 29375.415 15199.192 43551.64 0.0000045
## 2-1 21001.700 12028.626 29974.77 0.0000002
##
##
## $data
## depvar interrupt_var lag_depvar
## 2 2800 0 4780
## 3 4250 0 2800
## 4 11480 0 4250
## 5 38254 0 11480
## 6 3300 0 38254
## 7 47206 0 3300
## 8 27595 0 47206
## 9 36888 0 27595
## 10 1990 0 36888
## 11 30338 0 1990
## 12 16880 0 30338
## 13 9040 0 16880
## 14 9000 0 9040
## 15 25484 0 9000
## 16 15595 0 25484
## 17 8220 0 15595
## 18 4130 0 8220
## 19 17290 0 4130
## 20 14366 0 17290
## 21 22595 0 14366
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## 23 23886 0 12022
## 24 3200 0 23886
## 25 60883 0 3200
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## 27 10198 0 9950
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## 29 6621 0 37740
## 30 29115 0 6621
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## 32 12230 0 17992
## 33 23782 0 12230
## 34 73000 0 23782
## 35 38202 0 73000
## 36 4000 0 38202
## 37 10119 0 4000
## 38 29230 0 10119
## 39 43025 0 29230
## 40 26574 0 43025
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## 384 70711 2 9770
## 385 21990 2 70711
## 386 71844 2 21990
## 387 93642 2 71844
## 388 38840 2 93642
## 389 21990 2 38840
## 390 25730 2 21990
## 391 120555 2 25730
## 392 98000 2 120555
## 393 17444 2 98000
## 394 49314 2 17444
## 395 36666 2 49314
## 396 81125 2 36666
## 397 29924 2 81125
## 398 190020 2 29924
## 399 10700 2 190020
## 400 30000 2 10700
## 401 45789 2 30000
## 402 32530 2 45789
## 403 69000 2 32530
## 404 20000 2 69000
## 405 83912 2 20000
## 406 79500 2 83912
## 407 21990 2 79500
## 408 37505 2 21990
## 409 83826 2 37505
## 410 9240 2 83826
## 411 14480 2 9240
## 412 70503 2 14480
## 413 44860 2 70503
## 414 12620 2 44860
## 415 21990 2 12620
## 416 36500 2 21990
## 417 72328 2 36500
## 418 37930 2 72328
## 419 27810 2 37930
## 420 5500 2 27810
## 421 131793 2 5500
## 422 52800 2 131793
## 423 67987 2 52800
## 424 11980 2 67987
## 425 8467 2 11980
## 426 17200 2 8467
## 427 34240 2 17200
## 428 38990 2 34240
## 429 50630 2 38990
## 430 21980 2 50630
## 431 56774 2 21980
## 432 75200 2 56774
## 433 72430 2 75200
## 434 8320 2 72430
## 435 15500 2 8320
## 436 25990 2 15500
## 437 21084 2 25990
## 438 28549 2 21084
## 439 44255 2 28549
## 440 50265 2 44255
## 441 20950 2 50265
## 442 30320 2 20950
## 443 10000 2 30320
## 444 60079 2 10000
## 445 78277 2 60079
## 446 18230 2 78277
## 447 47909 2 18230
## 448 45042 2 47909
## 449 13380 2 45042
## 450 41970 2 13380
## 451 21990 2 41970
## 452 6414 2 21990
## 453 8914 2 6414
## 454 138918 2 8914
## 455 42490 2 138918
## 456 58666 2 42490
## 457 7000 2 58666
## 458 24792 2 7000
## 459 19400 2 24792
## 460 38710 2 19400
## 461 57775 2 38710
## 462 81350 2 57775
## 463 21990 2 81350
## 464 67655 2 21990
## 465 53000 2 67655
## 466 40400 2 53000
## 467 12502 2 40400
## 468 76533 2 12502
## 469 82965 2 76533
##
## $alpha
## [1] 0.05
##
## $itsa.result
## [1] "Significant variation between time periods with chosen alpha"
##
## $group.means
## interrupt_var count mean s.d.
## 1 0 40 20581.25 16591.08
## 2 1 120 29360.12 24311.11
## 3 2 309 50361.83 40474.01
##
## $dependent
## [1] 2800 4250 11480 38254 3300 47206 27595 36888 1990 30338
## [11] 16880 9040 9000 25484 15595 8220 4130 17290 14366 22595
## [21] 12022 23886 3200 60883 9950 10198 37740 6621 29115 17992
## [31] 12230 23782 73000 38202 4000 10119 29230 43025 26574 84292
## [41] 18254 31900 35826 5640 17470 17341 8804 78503 51424 44554
## [51] 60435 13420 54700 42750 19990 7900 4670 5500 63823 27300
## [61] 13082 55315 17993 7050 8800 30736 160249 27858 17220 51704
## [71] 46440 94286 14820 42000 7872 41403 6030 9998 19791 40474
## [81] 9352 24588 500 22180 3800 12000 25000 19591 53500 35900
## [91] 30112 4000 16942 58055 10220 61508 20390 23700 6950 20200
## [101] 6590 34380 5660 11224 8000 9352 25628 42430 27036 33795
## [111] 9541 21431 32404 5580 13931 29243 55000 96600 6950 47128
## [121] 29606 29604 10720 13500 67283 23640 33345 39074 90502 7855
## [131] 5980 42150 37913 76599 34589 20000 17990 31540 13388 24990
## [141] 69267 7000 31334 16144 13000 29610 13970 18000 31284 13919
## [151] 40000 17327 42354 15429 31255 41707 38216 3702 11421 22764
## [161] 62659 99054 28327 17101 43930 22708 50136 26327 61450 71800
## [171] 147732 61656 10000 24700 13500 83106 63980 85968 84281 39990
## [181] 24990 70624 7000 37945 12317 16485 68102 105950 66265 54700
## [191] 83980 98722 58817 17577 107500 122920 20055 48104 12532 10980
## [201] 28990 72200 60168 71537 33900 9631 14813 83431 143430 57200
## [211] 85358 47168 40010 90669 19990 48788 14000 13211 22790 64960
## [221] 10700 156000 107450 21227 195632 82848 81012 71530 83489 59359
## [231] 17877 168788 76129 118970 82462 38900 21000 63180 61796 12317
## [241] 54470 15245 72490 38250 10700 11150 94711 8370 46782 10500
## [251] 27880 39990 33778 21227 55952 77243 52126 49662 35924 116320
## [261] 161433 54470 23641 64000 19379 47512 66300 75490 22637 25319
## [271] 32803 89927 17648 99528 60398 55229 27691 81021 70970 95158
## [281] 50847 62081 42000 73709 30783 43436 11440 38000 200164 86313
## [291] 76990 10090 36123 43500 69333 47888 7000 29000 92796 62100
## [301] 36400 263220 95529 12350 65960 78361 22277 64180 62082 65569
## [311] 9784 16200 56000 51151 4500 51842 21990 40110 9640 86702
## [321] 35000 211129 34543 35000 104200 28770 290890 22000 17453 24910
## [331] 21837 39708 5710 50082 51549 20975 43500 16555 85920 14329
## [341] 12000 45254 21990 5990 64626 91236 16400 19018 59565 36590
## [351] 4000 8090 15654 59228 9891 56280 17490 41493 38174 51422
## [361] 44716 19990 73416 76750 45905 39790 71160 24550 15539 14480
## [371] 54287 15270 21990 39570 82950 33865 77669 5980 10840 10269
## [381] 35826 9770 70711 21990 71844 93642 38840 21990 25730 120555
## [391] 98000 17444 49314 36666 81125 29924 190020 10700 30000 45789
## [401] 32530 69000 20000 83912 79500 21990 37505 83826 9240 14480
## [411] 70503 44860 12620 21990 36500 72328 37930 27810 5500 131793
## [421] 52800 67987 11980 8467 17200 34240 38990 50630 21980 56774
## [431] 75200 72430 8320 15500 25990 21084 28549 44255 50265 20950
## [441] 30320 10000 60079 78277 18230 47909 45042 13380 41970 21990
## [451] 6414 8914 138918 42490 58666 7000 24792 19400 38710 57775
## [461] 81350 21990 67655 53000 40400 12502 76533 82965
##
## $interrupt_var
## [1] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [38] 0 0 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
## [75] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
## [112] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
## [149] 1 1 1 1 1 1 1 1 1 1 1 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
## [186] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
## [223] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
## [260] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
## [297] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
## [334] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
## [371] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
## [408] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
## [445] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
## Levels: 0 1 2
##
## $residuals
## 2 3 4 5 6 7
## -17617.53362 -16095.54964 -8918.26518 17592.88424 -18334.49910 26842.27259
## 8 9 10 11 12 13
## 5635.04607 15641.01464 -19594.83743 10021.89835 -4466.70861 -11817.43570
## 14 15 16 17 18 19
## -11572.40824 4913.04599 -5575.23880 -12590.71883 -16412.59669 -3103.90252
## 20 21 22 23 24 25
## -6506.34147 1828.96214 -9043.20764 3205.17953 -17912.14264 40522.90814
## 26 27 28 29 30 31
## -12507.18870 -10407.49178 17125.49204 -14994.81235 8630.53582 -3310.24578
## 32 33 34 35 36 37
## -8667.86307 3093.61758 51891.63833 15304.29117 -17632.60861 -10270.17629
## 38 39 40 41 42 43
## 8618.36413 21718.57333 4766.04860 55037.75689 -13098.61240 2948.23501
## 44 45 46 47 48 49
## 6378.12726 -23950.60461 -11023.17616 -11582.26224 -20114.57238 49894.79490
## 50 51 52 53 54 55
## 20281.84984 14396.32162 30527.08421 -17065.27819 25923.97771 12473.22086
## 56 57 58 59 60 61
## -9852.33039 -21114.87821 -23905.33968 -22957.91128 35334.91362 -3308.45077
## 62 63 64 65 66 67
## -16198.63724 26551.26589 -12306.13780 -21892.27619 -19744.43747 2127.94033
## 68 69 70 71 72 73
## 130843.44511 -6256.07046 -12080.92363 22789.82665 16272.14207 64309.51765
## 74 75 76 77 78 79
## -16895.94971 13173.07995 -21943.06374 12828.67827 -23763.35948 -18509.35482
## 80 81 82 83 84 85
## -8860.61362 11466.35654 -20407.58518 -4040.12793 -28682.04100 -6126.30866
## 86 87 88 89 90 91
## -25294.49685 -16426.28195 -3724.39742 -9606.01949 24499.62765 5666.84751
## 92 93 94 95 96 97
## 518.70508 -25382.86903 -11491.55306 29150.93349 -20178.75199 32848.31545
## 98 99 100 101 102 103
## -10134.28769 -5329.42043 -22199.75728 -8340.80192 -22432.51288 5852.28608
## 104 105 106 107 108 109
## -23878.03449 -17269.90327 -20696.18551 -19226.97524 -3000.12793 13210.14923
## 110 111 112 113 114 115
## -2794.69662 4523.96063 -19975.76650 -7203.99913 3336.73345 -23886.19594
## 116 117 118 119 120 121
## -14559.99482 448.40003 25648.72394 66312.31419 -24850.07644 18587.19808
## 122 123 124 125 126 127
## -395.49497 239.52688 -18644.40041 -15177.86232 38504.06927 -7094.24095
## 128 129 130 131 132 133
## 4197.42405 9573.59350 60793.31259 -23723.38033 -22593.70369 13644.46296
## 134 135 136 137 138 139
## 8092.48293 46932.52137 3516.07080 -9545.63280 -11025.24177 2597.83288
## 140 141 142 143 144 145
## -16046.78475 -3784.85891 40070.34407 -23806.37035 2791.38030 -13283.29550
## 146 147 148 149 150 151
## -15875.05479 849.24704 -15394.61855 -10796.01784 2341.46932 -15506.47773
## 152 153 154 155 156 157
## 11205.83630 -12415.35265 13435.93660 -14398.93360 2405.93942 12282.57658
## 158 159 160 161 162 163
## 8411.58844 -25975.49436 -17001.71911 -26190.52790 13292.09116 48236.68674
## 164 165 166 167 168 169
## -23813.47329 -32468.15473 -5231.02739 -27428.41028 771.12707 -24035.03279
## 170 171 172 173 174 175
## 11953.55636 21026.64059 96582.36071 7745.81154 -40780.84865 -24202.86667
## 176 177 178 179 180 181
## -35937.29317 34075.88892 12419.32493 35102.66106 32616.27536 -11613.39284
## 182 183 184 185 186 187
## -25003.16944 21176.16372 -44106.88517 -10848.80004 -37601.82235 -32502.10247
## 188 189 190 191 192 193
## 18963.36762 54934.80351 13873.81888 3751.58864 33452.04051 47129.55018
## 194 195 196 197 198 199
## 6688.59675 -33100.63526 58321.66737 70472.46779 -32953.13470 -1164.42167
## 200 201 202 203 204 205
## -37756.15832 -38014.91891 -19948.49511 22606.74155 9003.81849 20810.24840
## 206 207 208 209 210 211
## -17240.07778 -40140.76417 -34076.45148 34353.15409 91857.50938 3446.21309
## 212 213 214 215 216 217
## 34739.15165 -4474.54776 -10244.12953 40675.10345 -31845.63205 -478.05856
## 218 219 220 221 222 223
## -36313.02551 -35837.28885 -26229.60432 15592.14592 -40200.96737 107071.68445
## 224 225 226 227 228 229
## 53239.22390 -31218.71444 146320.96963 27196.38098 29460.70466 20045.45344
## 230 231 232 233 234 235
## 32349.17670 7784.40075 -32820.33997 119598.76071 21453.30920 67662.97756
## 236 237 238 239 240 241
## 29597.46970 -12637.26210 -28953.54189 13877.22234 10959.74550 -38468.93843
## 242 243 244 245 246 247
## 5482.89753 -35274.59771 23396.44850 -12924.72462 -39229.91079 -37778.31555
## 248 249 250 251 252 253
## 45766.32445 -43612.58116 -2061.60714 -39740.09629 -21041.04444 -9562.90380
## 254 255 256 257 258 259
## -16215.16944 -28540.32880 6640.96963 26669.52337 778.47748 -772.38032
## 260 261 262 263 264 265
## -14420.80026 66474.65220 108664.81189 61.70423 -26878.59771 14601.20735
## 266 267 268 269 270 271
## -31487.06605 -1731.84532 16033.36416 24540.31620 -28646.79125 -24043.29168
## 272 273 274 275 276 277
## -16656.79725 40195.11786 -34160.65624 50347.08613 8240.29418 4493.88662
## 278 279 280 281 282 283
## -22856.19157 31474.96740 19485.12624 44038.53581 -1151.83209 11693.11842
## 284 285 286 287 288 289
## -8796.29976 23642.75592 -20436.04203 -6222.44394 -38678.45064 -10955.21866
## 290 291 292 293 294 295
## 150243.17810 30496.61766 25312.73270 -41248.32457 -12783.13867 -6352.58255
## 296 297 298 299 300 301
## 19212.22260 -3171.95017 -43280.30552 -19793.80004 43202.37799 10187.03971
## 302 303 304 305 306 307
## -14396.99052 213357.34697 37420.18247 -39662.32000 16971.69780 27423.67708
## 308 309 310 311 312 313
## -29111.16802 14830.79631 11209.38995 14772.66388 -41139.10790 -32695.01388
## 314 315 316 317 318 319
## 6871.72895 575.77830 -45898.93366 3139.08882 -28434.05535 -9228.76965
## 320 321 322 323 324 325
## -40357.53210 37812.22132 -16691.40961 161317.24473 -21672.02088 -14795.14079
## 326 327 328 329 330 331
## 54388.24473 -23557.55892 241304.73977 -37114.77543 -31886.13320 -24263.82454
## 332 333 334 335 336 337
## -27607.92783 -9625.20725 -44272.91717 1335.09861 1188.93041 -29438.40317
## 338 339 340 341 342 343
## -5801.86877 -33565.77740 36778.82274 -37333.97957 -37060.24982 -3721.57776
## 344 345 346 347 348 349
## -28194.54502 -43348.76965 15868.91906 40347.17538 -35456.24565 -30117.54215
## 350 351 352 353 354 355
## 10334.27903 -14114.82921 -45869.56059 -40594.73341 -33179.42758 10119.57908
## 356 357 358 359 360 361
## -40801.57739 7381.09608 -33095.40125 -7682.16970 -11873.81182 1494.85223
## 362 363 364 365 366 367
## -5692.78602 -30174.98574 24149.94144 25541.61015 -5424.59924 -10418.21248
## 368 369 370 371 372 373
## 21174.10167 -26576.37174 -33892.83984 -34624.24003 5221.26049 -35242.94465
## 374 375 376 377 378 379
## -27104.46039 -9768.76965 32972.09989 -17690.00361 27898.50827 -45383.00998
## 380 381 382 383 384 385
## -37916.71739 -38664.40533 -13086.64631 -40071.78495 21816.49510 -29120.04811
## 386 387 388 389 390 391
## 22505.23035 42490.76106 -13103.71708 -27961.36056 -23608.76965 71080.26062
## 392 393 394 395 396 397
## 45077.84616 -34658.15455 140.50266 -13666.14853 31252.67639 -21564.65474
## 398 399 400 401 402 403
## 140392.78547 -44747.59171 -18928.31555 -3840.97755 -17673.99524 19278.04292
## 404 405 406 407 408 409
## -31047.84377 34645.57789 27910.02236 -29439.57698 -11833.76965 33923.17409
## 410 411 412 413 414 415
## -42346.85106 -34395.23646 21437.26049 -6242.48615 -37550.22094 -27008.11820
## 416 417 418 419 420 421
## -12838.76965 22461.71141 -13238.83502 -22108.27702 -44050.35891 83053.73328
## 422 423 424 425 426 427
## -530.71744 17528.11604 -39031.01560 -40507.85065 -31647.13362 -14924.62659
## 428 429 430 431 432 433
## -10794.12506 673.18611 -28399.99243 7435.59391 24596.63911 21156.75186
## 434 435 436 437 438 439
## -42852.54329 -33341.78936 -23112.82216 -28400.19182 -20756.83152 -5322.22566
## 440 441 442 443 444 445
## 116.77417 -29416.72265 -18980.95988 -39641.61133 11176.13333 27553.48404
## 446 447 448 449 450 451
## -33155.11415 -1293.07280 -5239.06899 -36796.83765 -7055.74841 -28075.15341
## 452 453 454 455 456 457
## -42924.76965 -39858.49569 90054.61545 -11099.75070 8581.94170 -43672.14558
## 458 459 460 461 462 463
## -24001.80004 -30040.63788 -10534.60879 7828.36566 30710.24721 -29506.83474
## 464 465 466 467 468 469
## 18316.23035 2001.05444 -10066.15507 -37506.07521 27539.17176 31643.28992
##
## $fitted.values
## 2 3 4 5 6 7 8 9
## 20417.53 20345.55 20398.27 20661.12 21634.50 20363.73 21959.95 21246.99
## 10 11 12 13 14 15 16 17
## 21584.84 20316.10 21346.71 20857.44 20572.41 20570.95 21170.24 20810.72
## 18 19 20 21 22 23 24 25
## 20542.60 20393.90 20872.34 20766.04 21065.21 20680.82 21112.14 20360.09
## 26 27 28 29 30 31 32 33
## 22457.19 20605.49 20614.51 21615.81 20484.46 21302.25 20897.86 20688.38
## 34 35 36 37 38 39 40 41
## 21108.36 22897.71 21632.61 20389.18 20611.64 21306.43 21807.95 29254.24
## 42 43 44 45 46 47 48 49
## 31352.61 28951.76 29447.87 29590.60 28493.18 28923.26 28918.57 28608.21
## 50 51 52 53 54 55 56 57
## 31142.15 30157.68 29907.92 30485.28 28776.02 30276.78 29842.33 29014.88
## 58 59 60 61 62 63 64 65
## 28575.34 28457.91 28488.09 30608.45 29280.64 28763.73 30299.14 28942.28
## 66 67 68 69 70 71 72 73
## 28544.44 28608.06 29405.55 34114.07 29300.92 28914.17 30167.86 29976.48
## 74 75 76 77 78 79 80 81
## 31715.95 28826.92 29815.06 28574.32 29793.36 28507.35 28651.61 29007.64
## 82 83 84 85 86 87 88 89
## 29759.59 28628.13 29182.04 28306.31 29094.50 28426.28 28724.40 29197.02
## 90 91 92 93 94 95 96 97
## 29000.37 30233.15 29593.29 29382.87 28433.55 28904.07 30398.75 28659.68
## 98 99 100 101 102 103 104 105
## 30524.29 29029.42 29149.76 28540.80 29022.51 28527.71 29538.03 28493.90
## 106 107 108 109 110 111 112 113
## 28696.19 28578.98 28628.13 29219.85 29830.70 29271.04 29516.77 28635.00
## 114 115 116 117 118 119 120 121
## 29067.27 29466.20 28490.99 28794.60 29351.28 30287.69 31800.08 28540.80
## 122 123 124 125 126 127 128 129
## 30001.49 29364.47 29364.40 28677.86 28778.93 30734.24 29147.58 29500.41
## 130 131 132 133 134 135 136 137
## 29708.69 31578.38 28573.70 28505.54 29820.52 29666.48 31072.93 29545.63
## 138 139 140 141 142 143 144 145
## 29015.24 28942.17 29434.78 28774.86 29196.66 30806.37 28542.62 29427.30
## 146 147 148 149 150 151 152 153
## 28875.05 28760.75 29364.62 28796.02 28942.53 29425.48 28794.16 29742.35
## 154 155 156 157 158 159 160 161
## 28918.06 29827.93 28849.06 29424.42 29804.41 29677.49 28422.72 48954.53
## 162 163 164 165 166 167 168 169
## 49366.91 50817.31 52140.47 49569.15 49161.03 50136.41 49364.87 50362.03
## 170 171 172 173 174 175 176 177
## 49496.44 50773.36 51149.64 53910.19 50780.85 48902.87 49437.29 49030.11
## 178 179 180 181 182 183 184 185
## 51560.68 50865.34 51664.72 51603.39 49993.17 49447.84 51106.89 48793.80
## 186 187 188 189 190 191 192 193
## 49918.82 48987.10 49138.63 51015.20 52391.18 50948.41 50527.96 51592.45
## 194 195 196 197 198 199 200 201
## 52128.40 50677.64 49178.33 52447.53 53008.13 49268.42 50288.16 48994.92
## 202 203 204 205 206 207 208 209
## 48938.50 49593.26 51164.18 50726.75 51140.08 49771.76 48889.45 49077.85
## 210 211 212 213 214 215 216 217
## 51572.49 53753.79 50618.85 51642.55 50254.13 49993.90 51835.63 49266.06
## 218 219 220 221 222 223 224 225
## 50313.03 49048.29 49019.60 49367.85 50900.97 48928.32 54210.78 52445.71
## 226 227 228 229 230 231 232 233
## 49311.03 55651.62 51551.30 51484.55 51139.82 51574.60 50697.34 49189.24
## 234 235 236 237 238 239 240 241
## 54675.69 51307.02 52864.53 51537.26 49953.54 49302.78 50836.25 50785.94
## 242 243 244 245 246 247 248 249
## 48987.10 50519.60 49093.55 51174.72 49929.91 48928.32 48944.68 51982.58
## 250 251 252 253 254 255 256 257
## 48843.61 50240.10 48921.04 49552.90 49993.17 49767.33 49311.03 50573.48
## 258 259 260 261 262 263 264 265
## 51347.52 50434.38 50344.80 49845.35 52768.19 54408.30 50519.60 49398.79
## 266 267 268 269 270 271 272 273
## 50866.07 49243.85 50266.64 50949.68 51283.79 49362.29 49459.80 49731.88
## 274 275 276 277 278 279 280 281
## 51808.66 49180.91 52157.71 50735.11 50547.19 49546.03 51484.87 51119.46
## 282 283 284 285 286 287 288 289
## 51998.83 50387.88 50796.30 50066.24 51219.04 49658.44 50118.45 48955.22
## 290 291 292 293 294 295 296 297
## 49920.82 55816.38 51677.27 51338.32 48906.14 49852.58 50120.78 51059.95
## 298 299 300 301 302 303 304 305
## 50280.31 48793.80 49593.62 51912.96 50796.99 49862.65 58108.82 52012.32
## 306 307 308 309 310 311 312 313
## 48988.30 50937.32 51388.17 49349.20 50872.61 50796.34 50923.11 48895.01
## 314 315 316 317 318 319 320 321
## 49128.27 50575.22 50398.93 48702.91 50424.06 49338.77 49997.53 48889.78
## 322 323 324 325 326 327 328 329
## 51691.41 49811.76 56215.02 49795.14 49811.76 52327.56 49585.26 59114.78
## 330 331 332 333 334 335 336 337
## 49339.13 49173.82 49444.93 49333.21 49982.92 48746.90 50360.07 50413.40
## 338 339 340 341 342 343 344 345
## 49301.87 50120.78 49141.18 51662.98 49060.25 48975.58 50184.55 49338.77
## 346 347 348 349 350 351 352 353
## 48757.08 50888.82 51856.25 49135.54 49230.72 50704.83 49869.56 48684.73
## 354 355 356 357 358 359 360 361
## 48833.43 49108.42 50692.58 48898.90 50585.40 49175.17 50047.81 49927.15
## 362 363 364 365 366 367 368 369
## 50408.79 50164.99 49266.06 51208.39 51329.60 50208.21 49985.90 51126.37
## 370 371 372 373 374 375 376 377
## 49431.84 49104.24 49065.74 50512.94 49094.46 49338.77 49977.90 51555.00
## 378 379 380 381 382 383 384 385
## 49770.49 51363.01 48756.72 48933.41 48912.65 49841.78 48894.50 51110.05
## 386 387 388 389 390 391 392 393
## 49338.77 51151.24 51943.72 49951.36 49338.77 49474.74 52922.15 52102.15
## 394 395 396 397 398 399 400 401
## 49173.50 50332.15 49872.32 51488.65 49627.21 55447.59 48928.32 49629.98
## 402 403 404 405 406 407 408 409
## 50204.00 49721.96 51047.84 49266.42 51589.98 51429.58 49338.77 49902.83
## 410 411 412 413 414 415 416 417
## 51586.85 48875.24 49065.74 51102.49 50170.22 48998.12 49338.77 49866.29
## 418 419 420 421 422 423 424 425
## 51168.84 49918.28 49550.36 48739.27 53330.72 50458.88 51011.02 48974.85
## 426 427 428 429 430 431 432 433
## 48847.13 49164.63 49784.13 49956.81 50379.99 49338.41 50603.36 51273.25
## 434 435 436 437 438 439 440 441
## 51172.54 48841.79 49102.82 49484.19 49305.83 49577.23 50148.23 50366.72
## 442 443 444 445 446 447 448 449
## 49300.96 49641.61 48902.87 50723.52 51385.11 49202.07 50281.07 50176.84
## 450 451 452 453 454 455 456 457
## 49025.75 50065.15 49338.77 48772.50 48863.38 53589.75 50084.06 50672.15
## 458 459 460 461 462 463 464 465
## 48793.80 49440.64 49244.61 49946.63 50639.75 51496.83 49338.77 50998.95
## 466 467 468 469
## 50466.16 50008.08 48993.83 51321.71
##
## $shapiro.test
## [1] 0
##
## $levenes.test
## [1] 0
##
## $autcorr
## [1] "No autocorrelation evidence"
##
## $post_sums
## [1] "Post-Est Warning"
##
## $adjr_sq
## [1] 0.0853
##
## $fstat.bootstrap
##
## ORDINARY NONPARAMETRIC BOOTSTRAP
##
##
## Call:
## boot::boot(data = x, statistic = f.stat, R = Reps, formula = depvar ~
## ., parallel = parr)
##
##
## Bootstrap Statistics :
## original bias std. error
## t1* 19.4333579 0.5793671 4.757213
## t2* 0.6144496 1.0240832 2.266514
## WARNING: All values of t3* are NA
##
## $itsa.plot
##
## $booted.ints
## Parameter Lower CI Median F-value Upper CI
## 1 interrupt_var 12.791471632 19.7256159 28.341956
## 2 lag_depvar 0.006510307 0.7545766 6.307942itsa_metro_region_quar2<-
its.analysis::itsa.model(time = "day", depvar = "trend",data=tsdata_gastos_trend,
interrupt_var = "covid",
alpha = 0.05,no.plots = F, bootstrap = TRUE, Reps = 10000, print = F) 
print(itsa_metro_region_quar2)## [[1]]
## [1] "ITSA Model Fit"
##
## $aov.result
## Anova Table (Type II tests)
##
## Response: depvar
## Sum Sq Df F value Pr(>F)
## interrupt_var 4.1737e+08 2 4.5777 0.0108 *
## lag_depvar 7.3919e+10 1 1621.5153 <2e-16 ***
## Residuals 2.0879e+10 458
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## $tukey.result
## Tukey multiple comparisons of means
## 95% family-wise confidence level
##
## Fit: stats::aov(formula = x$depvar ~ x$interrupt_var)
##
## $`x$interrupt_var`
## diff lwr upr p adj
## 1-0 7228.838 807.4497 13650.23 0.0228181
## 2-0 27949.925 21995.9148 33903.94 0.0000000
## 2-1 20721.087 17081.4175 24360.76 0.0000000
##
##
## $data
## depvar interrupt_var lag_depvar
## 2 19269.29 0 16010.00
## 3 24139.00 0 19269.29
## 4 23816.14 0 24139.00
## 5 26510.14 0 23816.14
## 6 23456.71 0 26510.14
## 7 24276.71 0 23456.71
## 8 18818.71 0 24276.71
## 9 18517.14 0 18818.71
## 10 15475.29 0 18517.14
## 11 16365.29 0 15475.29
## 12 12621.29 0 16365.29
## 13 12679.86 0 12621.29
## 14 13440.71 0 12679.86
## 15 15382.86 0 13440.71
## 16 13459.71 0 15382.86
## 17 14644.14 0 13459.71
## 18 13927.00 0 14644.14
## 19 22034.57 0 13927.00
## 20 20986.00 0 22034.57
## 21 20390.57 0 20986.00
## 22 22554.14 0 20390.57
## 23 21782.57 0 22554.14
## 24 22529.57 0 21782.57
## 25 24642.71 0 22529.57
## 26 17692.29 0 24642.71
## 27 19668.29 0 17692.29
## 28 28640.00 0 19668.29
## 29 28706.00 0 28640.00
## 30 28331.57 0 28706.00
## 31 25617.86 0 28331.57
## 32 27223.29 0 25617.86
## 33 31622.57 0 27223.29
## 34 32021.43 0 31622.57
## 35 33634.57 0 32021.43
## 36 30784.86 0 33634.57
## 37 34770.57 0 30784.86
## 38 38443.00 1 34770.57
## 39 35073.00 1 38443.00
## 40 31422.29 1 35073.00
## 41 30103.29 1 31422.29
## 42 19319.29 1 30103.29
## 43 27926.29 1 19319.29
## 44 30715.43 1 27926.29
## 45 31962.29 1 30715.43
## 46 39790.14 1 31962.29
## 47 39211.57 1 39790.14
## 48 44548.57 1 39211.57
## 49 49398.00 1 44548.57
## 50 41039.00 1 49398.00
## 51 34821.29 1 41039.00
## 52 29123.57 1 34821.29
## 53 21275.71 1 29123.57
## 54 28476.14 1 21275.71
## 55 24561.86 1 28476.14
## 56 20323.57 1 24561.86
## 57 25370.00 1 20323.57
## 58 26811.86 1 25370.00
## 59 27151.86 1 26811.86
## 60 27623.29 1 27151.86
## 61 22896.57 1 27623.29
## 62 41889.29 1 22896.57
## 63 44000.14 1 41889.29
## 64 38558.00 1 44000.14
## 65 43373.86 1 38558.00
## 66 49001.00 1 43373.86
## 67 61213.29 1 49001.00
## 68 58939.57 1 61213.29
## 69 42046.86 1 58939.57
## 70 39191.71 1 42046.86
## 71 42646.43 1 39191.71
## 72 36121.57 1 42646.43
## 73 30915.57 1 36121.57
## 74 20273.43 1 30915.57
## 75 23938.29 1 20273.43
## 76 19274.29 1 23938.29
## 77 21662.29 1 19274.29
## 78 15819.00 1 21662.29
## 79 18126.14 1 15819.00
## 80 17240.71 1 18126.14
## 81 16127.71 1 17240.71
## 82 13917.14 1 16127.71
## 83 15379.86 1 13917.14
## 84 19510.14 1 15379.86
## 85 24567.29 1 19510.14
## 86 25700.43 1 24567.29
## 87 25729.00 1 25700.43
## 88 26435.00 1 25729.00
## 89 31157.14 1 26435.00
## 90 29818.43 1 31157.14
## 91 30962.43 1 29818.43
## 92 28746.71 1 30962.43
## 93 27830.71 1 28746.71
## 94 28252.14 1 27830.71
## 95 28717.57 1 28252.14
## 96 21365.43 1 28717.57
## 97 24816.86 1 21365.43
## 98 16838.57 1 24816.86
## 99 15529.14 1 16838.57
## 100 13286.29 1 15529.14
## 101 13629.43 1 13286.29
## 102 14404.86 1 13629.43
## 103 19524.86 1 14404.86
## 104 18475.71 1 19524.86
## 105 22495.00 1 18475.71
## 106 22254.57 1 22495.00
## 107 24173.29 1 22254.57
## 108 27466.43 1 24173.29
## 109 24602.43 1 27466.43
## 110 20531.14 1 24602.43
## 111 20846.43 1 20531.14
## 112 23875.71 1 20846.43
## 113 36312.71 1 23875.71
## 114 34244.00 1 36312.71
## 115 36347.43 1 34244.00
## 116 39779.71 1 36347.43
## 117 42018.71 1 39779.71
## 118 39372.57 1 42018.71
## 119 33444.00 1 39372.57
## 120 29255.86 1 33444.00
## 121 31640.14 1 29255.86
## 122 29671.14 1 31640.14
## 123 31023.71 1 29671.14
## 124 39723.43 1 31023.71
## 125 39314.14 1 39723.43
## 126 38239.86 1 39314.14
## 127 34649.43 1 38239.86
## 128 36688.43 1 34649.43
## 129 42867.57 1 36688.43
## 130 42226.86 1 42867.57
## 131 32155.14 1 42226.86
## 132 33603.00 1 32155.14
## 133 37254.43 1 33603.00
## 134 33145.57 1 37254.43
## 135 31299.43 1 33145.57
## 136 30252.00 1 31299.43
## 137 26310.71 1 30252.00
## 138 27929.86 1 26310.71
## 139 27666.14 1 27929.86
## 140 25017.57 1 27666.14
## 141 27335.00 1 25017.57
## 142 25760.71 1 27335.00
## 143 18436.86 1 25760.71
## 144 21906.00 1 18436.86
## 145 19418.14 1 21906.00
## 146 22826.14 1 19418.14
## 147 23444.29 1 22826.14
## 148 25264.86 1 23444.29
## 149 25473.29 1 25264.86
## 150 27366.86 1 25473.29
## 151 28855.86 1 27366.86
## 152 32326.86 1 28855.86
## 153 27141.43 1 32326.86
## 154 26297.71 1 27141.43
## 155 23499.14 1 26297.71
## 156 30246.29 1 23499.14
## 157 39931.86 1 30246.29
## 158 38020.43 2 39931.86
## 159 35004.00 2 38020.43
## 160 40750.86 2 35004.00
## 161 42363.29 2 40750.86
## 162 46273.57 2 42363.29
## 163 41083.29 2 46273.57
## 164 35711.29 2 41083.29
## 165 41921.71 2 35711.29
## 166 60583.29 2 41921.71
## 167 63115.57 2 60583.29
## 168 61300.14 2 63115.57
## 169 57666.43 2 61300.14
## 170 55834.00 2 57666.43
## 171 58927.71 2 55834.00
## 172 57810.57 2 58927.71
## 173 48987.14 2 57810.57
## 174 52219.29 2 48987.14
## 175 56503.57 2 52219.29
## 176 56545.00 2 56503.57
## 177 64705.57 2 56545.00
## 178 53833.29 2 64705.57
## 179 50114.00 2 53833.29
## 180 39592.43 2 50114.00
## 181 29907.29 2 39592.43
## 182 33923.29 2 29907.29
## 183 45489.00 2 33923.29
## 184 44866.29 2 45489.00
## 185 51680.57 2 44866.29
## 186 58257.00 2 51680.57
## 187 70600.57 2 58257.00
## 188 76648.00 2 70600.57
## 189 69430.14 2 76648.00
## 190 69651.57 2 69430.14
## 191 77745.14 2 69651.57
## 192 72795.86 2 77745.14
## 193 67670.71 2 72795.86
## 194 55357.86 2 67670.71
## 195 48524.00 2 55357.86
## 196 50154.43 2 48524.00
## 197 45111.57 2 50154.43
## 198 36147.00 2 45111.57
## 199 43501.57 2 36147.00
## 200 41472.43 2 43501.57
## 201 41058.00 2 41472.43
## 202 41605.57 2 41058.00
## 203 49382.86 2 41605.57
## 204 59558.57 2 49382.86
## 205 59134.57 2 59558.57
## 206 61109.00 2 59134.57
## 207 63004.43 2 61109.00
## 208 67344.29 2 63004.43
## 209 78180.86 2 67344.29
## 210 69117.86 2 78180.86
## 211 55597.57 2 69117.86
## 212 49426.14 2 55597.57
## 213 39119.43 2 49426.14
## 214 35636.86 2 39119.43
## 215 39201.14 2 35636.86
## 216 27777.00 2 39201.14
## 217 47207.00 2 27777.00
## 218 55587.29 2 47207.00
## 219 56619.71 2 55587.29
## 220 82679.86 2 56619.71
## 221 91259.57 2 82679.86
## 222 93552.71 2 91259.57
## 223 102242.71 2 93552.71
## 224 91884.00 2 102242.71
## 225 85013.86 2 91884.00
## 226 84535.29 2 85013.86
## 227 80700.43 2 84535.29
## 228 79740.57 2 80700.43
## 229 85163.14 2 79740.57
## 230 86724.86 2 85163.14
## 231 80355.00 2 86724.86
## 232 74875.14 2 80355.00
## 233 81347.00 2 74875.14
## 234 66062.43 2 81347.00
## 235 56946.43 2 66062.43
## 236 47732.14 2 56946.43
## 237 38129.71 2 47732.14
## 238 42928.29 2 38129.71
## 239 45392.57 2 42928.29
## 240 37895.43 2 45392.57
## 241 30660.29 2 37895.43
## 242 42430.86 2 30660.29
## 243 35845.14 2 42430.86
## 244 40350.43 2 35845.14
## 245 31494.71 2 40350.43
## 246 30013.29 2 31494.71
## 247 34197.57 2 30013.29
## 248 37430.14 2 34197.57
## 249 26932.43 2 37430.14
## 250 33729.86 2 26932.43
## 251 38081.43 2 33729.86
## 252 44028.00 2 38081.43
## 253 47139.71 2 44028.00
## 254 46558.86 2 47139.71
## 255 58350.57 2 46558.86
## 256 78380.00 2 58350.57
## 257 78168.29 2 78380.00
## 258 70510.86 2 78168.29
## 259 72207.14 2 70510.86
## 260 67881.00 2 72207.14
## 261 69536.43 2 67881.00
## 262 62390.71 2 69536.43
## 263 50113.14 2 62390.71
## 264 45565.57 2 50113.14
## 265 45805.29 2 45565.57
## 266 41348.57 2 45805.29
## 267 51426.86 2 41348.57
## 268 47160.57 2 51426.86
## 269 51907.43 2 47160.57
## 270 49751.43 2 51907.43
## 271 54407.43 2 49751.43
## 272 54746.29 2 54407.43
## 273 61634.57 2 54746.29
## 274 58926.43 2 61634.57
## 275 69999.29 2 58926.43
## 276 63044.86 2 69999.29
## 277 63285.29 2 63044.86
## 278 61395.43 2 63285.29
## 279 67969.43 2 61395.43
## 280 60792.57 2 67969.43
## 281 56859.14 2 60792.57
## 282 44899.43 2 56859.14
## 283 43064.14 2 44899.43
## 284 62790.29 2 43064.14
## 285 69120.71 2 62790.29
## 286 69589.43 2 69120.71
## 287 66633.29 2 69589.43
## 288 65588.57 2 66633.29
## 289 70168.57 2 65588.57
## 290 74644.71 2 70168.57
## 291 52891.00 2 74644.71
## 292 41560.57 2 52891.00
## 293 34704.86 2 41560.57
## 294 46520.00 2 34704.86
## 295 50231.00 2 46520.00
## 296 49216.71 2 50231.00
## 297 76914.86 2 49216.71
## 298 83720.71 2 76914.86
## 299 84485.00 2 83720.71
## 300 89765.00 2 84485.00
## 301 87702.86 2 89765.00
## 302 82013.86 2 87702.86
## 303 85982.43 2 82013.86
## 304 57248.43 2 85982.43
## 305 52968.43 2 57248.43
## 306 52601.86 2 52968.43
## 307 45493.29 2 52601.86
## 308 42298.86 2 45493.29
## 309 46423.71 2 42298.86
## 310 37898.00 2 46423.71
## 311 36435.14 2 37898.00
## 312 30209.57 2 36435.14
## 313 34541.86 2 30209.57
## 314 33604.71 2 34541.86
## 315 37990.71 2 33604.71
## 316 35683.43 2 37990.71
## 317 65201.86 2 35683.43
## 318 62730.57 2 65201.86
## 319 64589.14 2 62730.57
## 320 73744.86 2 64589.14
## 321 76477.71 2 73744.86
## 322 105647.43 2 76477.71
## 323 103790.29 2 105647.43
## 324 76122.29 2 103790.29
## 325 74746.14 2 76122.29
## 326 72865.71 2 74746.14
## 327 63652.57 2 72865.71
## 328 60358.29 2 63652.57
## 329 25957.14 2 60358.29
## 330 30178.43 2 25957.14
## 331 30681.57 2 30178.43
## 332 33337.29 2 30681.57
## 333 32582.71 2 33337.29
## 334 39184.43 2 32582.71
## 335 40415.71 2 39184.43
## 336 34975.43 2 40415.71
## 337 34076.14 2 34975.43
## 338 34221.14 2 34076.14
## 339 28862.57 2 34221.14
## 340 35729.86 2 28862.57
## 341 36489.29 2 35729.86
## 342 36785.14 2 36489.29
## 343 37787.71 2 36785.14
## 344 39832.14 2 37787.71
## 345 41917.86 2 39832.14
## 346 41633.57 2 41917.86
## 347 33557.00 2 41633.57
## 348 22759.57 2 33557.00
## 349 28877.86 2 22759.57
## 350 27574.00 2 28877.86
## 351 27104.71 2 27574.00
## 352 24376.14 2 27104.71
## 353 29732.29 2 24376.14
## 354 34030.00 2 29732.29
## 355 39139.71 2 34030.00
## 356 37066.57 2 39139.71
## 357 38509.29 2 37066.57
## 358 40957.29 2 38509.29
## 359 49423.00 2 40957.29
## 360 50053.29 2 49423.00
## 361 50284.14 2 50053.29
## 362 53103.86 2 50284.14
## 363 50223.00 2 53103.86
## 364 49587.14 2 50223.00
## 365 41167.71 2 49587.14
## 366 37958.71 2 41167.71
## 367 33582.29 2 37958.71
## 368 31039.43 2 33582.29
## 369 26526.57 2 31039.43
## 370 34869.43 2 26526.57
## 371 37487.43 2 34869.43
## 372 46514.43 2 37487.43
## 373 39613.43 2 46514.43
## 374 38980.57 2 39613.43
## 375 37306.14 2 38980.57
## 376 36771.29 2 37306.14
## 377 26317.00 2 36771.29
## 378 31580.71 2 26317.00
## 379 23626.57 2 31580.71
## 380 33035.71 2 23626.57
## 381 44864.57 2 33035.71
## 382 48946.14 2 44864.57
## 383 46969.57 2 48946.14
## 384 49249.57 2 46969.57
## 385 56370.14 2 49249.57
## 386 67228.71 2 56370.14
## 387 59457.29 2 67228.71
## 388 53124.71 2 59457.29
## 389 52814.14 2 53124.71
## 390 61262.00 2 52814.14
## 391 61861.14 2 61262.00
## 392 71784.71 2 61861.14
## 393 59313.29 2 71784.71
## 394 61107.00 2 59313.29
## 395 60603.43 2 61107.00
## 396 60012.57 2 60603.43
## 397 58280.43 2 60012.57
## 398 56862.71 2 58280.43
## 399 41704.43 2 56862.71
## 400 51533.00 2 41704.43
## 401 50388.71 2 51533.00
## 402 49205.29 2 50388.71
## 403 56533.29 2 49205.29
## 404 47996.14 2 56533.29
## 405 47207.57 2 47996.14
## 406 45292.00 2 47207.57
## 407 40343.43 2 45292.00
## 408 39004.86 2 40343.43
## 409 36788.43 2 39004.86
## 410 30027.57 2 36788.43
## 411 39040.14 2 30027.57
## 412 42390.14 2 39040.14
## 413 36291.14 2 42390.14
## 414 30668.29 2 36291.14
## 415 47693.00 2 30668.29
## 416 52094.43 2 47693.00
## 417 56592.57 2 52094.43
## 418 47971.43 2 56592.57
## 419 43762.43 2 47971.43
## 420 42246.71 2 43762.43
## 421 46352.43 2 42246.71
## 422 33094.86 2 46352.43
## 423 32784.86 2 33094.86
## 424 26212.43 2 32784.86
## 425 32611.57 2 26212.43
## 426 42144.86 2 32611.57
## 427 50034.86 2 42144.86
## 428 46332.00 2 50034.86
## 429 42976.29 2 46332.00
## 430 39456.29 2 42976.29
## 431 39328.29 2 39456.29
## 432 35296.14 2 39328.29
## 433 30875.43 2 35296.14
## 434 27709.00 2 30875.43
## 435 29513.29 2 27709.00
## 436 31630.43 2 29513.29
## 437 29346.14 2 31630.43
## 438 34916.86 2 29346.14
## 439 42020.86 2 34916.86
## 440 38303.00 2 42020.86
## 441 37966.43 2 38303.00
## 442 41408.14 2 37966.43
## 443 38988.14 2 41408.14
## 444 43555.29 2 38988.14
## 445 38114.00 2 43555.29
## 446 27847.86 2 38114.00
## 447 26517.00 2 27847.86
## 448 39518.29 2 26517.00
## 449 39153.71 2 39518.29
## 450 45623.14 2 39153.71
## 451 40627.43 2 45623.14
## 452 41027.71 2 40627.43
## 453 42882.86 2 41027.71
## 454 47139.43 2 42882.86
## 455 35547.57 2 47139.43
## 456 41099.00 2 35547.57
## 457 35859.57 2 41099.00
## 458 44524.57 2 35859.57
## 459 48554.29 2 44524.57
## 460 51554.29 2 48554.29
## 461 47810.29 2 51554.29
## 462 50490.00 2 47810.29
## 463 50720.71 2 50490.00
##
## $alpha
## [1] 0.05
##
## $itsa.result
## [1] "Significant variation between time periods with chosen alpha"
##
## $group.means
## interrupt_var count mean s.d.
## 1 0 37 22066.04 6308.636
## 2 1 120 29463.10 9187.258
## 3 2 306 50184.19 16531.992
##
## $dependent
## [1] 19269.29 24139.00 23816.14 26510.14 23456.71 24276.71 18818.71
## [8] 18517.14 15475.29 16365.29 12621.29 12679.86 13440.71 15382.86
## [15] 13459.71 14644.14 13927.00 22034.57 20986.00 20390.57 22554.14
## [22] 21782.57 22529.57 24642.71 17692.29 19668.29 28640.00 28706.00
## [29] 28331.57 25617.86 27223.29 31622.57 32021.43 33634.57 30784.86
## [36] 34770.57 38443.00 35073.00 31422.29 30103.29 19319.29 27926.29
## [43] 30715.43 31962.29 39790.14 39211.57 44548.57 49398.00 41039.00
## [50] 34821.29 29123.57 21275.71 28476.14 24561.86 20323.57 25370.00
## [57] 26811.86 27151.86 27623.29 22896.57 41889.29 44000.14 38558.00
## [64] 43373.86 49001.00 61213.29 58939.57 42046.86 39191.71 42646.43
## [71] 36121.57 30915.57 20273.43 23938.29 19274.29 21662.29 15819.00
## [78] 18126.14 17240.71 16127.71 13917.14 15379.86 19510.14 24567.29
## [85] 25700.43 25729.00 26435.00 31157.14 29818.43 30962.43 28746.71
## [92] 27830.71 28252.14 28717.57 21365.43 24816.86 16838.57 15529.14
## [99] 13286.29 13629.43 14404.86 19524.86 18475.71 22495.00 22254.57
## [106] 24173.29 27466.43 24602.43 20531.14 20846.43 23875.71 36312.71
## [113] 34244.00 36347.43 39779.71 42018.71 39372.57 33444.00 29255.86
## [120] 31640.14 29671.14 31023.71 39723.43 39314.14 38239.86 34649.43
## [127] 36688.43 42867.57 42226.86 32155.14 33603.00 37254.43 33145.57
## [134] 31299.43 30252.00 26310.71 27929.86 27666.14 25017.57 27335.00
## [141] 25760.71 18436.86 21906.00 19418.14 22826.14 23444.29 25264.86
## [148] 25473.29 27366.86 28855.86 32326.86 27141.43 26297.71 23499.14
## [155] 30246.29 39931.86 38020.43 35004.00 40750.86 42363.29 46273.57
## [162] 41083.29 35711.29 41921.71 60583.29 63115.57 61300.14 57666.43
## [169] 55834.00 58927.71 57810.57 48987.14 52219.29 56503.57 56545.00
## [176] 64705.57 53833.29 50114.00 39592.43 29907.29 33923.29 45489.00
## [183] 44866.29 51680.57 58257.00 70600.57 76648.00 69430.14 69651.57
## [190] 77745.14 72795.86 67670.71 55357.86 48524.00 50154.43 45111.57
## [197] 36147.00 43501.57 41472.43 41058.00 41605.57 49382.86 59558.57
## [204] 59134.57 61109.00 63004.43 67344.29 78180.86 69117.86 55597.57
## [211] 49426.14 39119.43 35636.86 39201.14 27777.00 47207.00 55587.29
## [218] 56619.71 82679.86 91259.57 93552.71 102242.71 91884.00 85013.86
## [225] 84535.29 80700.43 79740.57 85163.14 86724.86 80355.00 74875.14
## [232] 81347.00 66062.43 56946.43 47732.14 38129.71 42928.29 45392.57
## [239] 37895.43 30660.29 42430.86 35845.14 40350.43 31494.71 30013.29
## [246] 34197.57 37430.14 26932.43 33729.86 38081.43 44028.00 47139.71
## [253] 46558.86 58350.57 78380.00 78168.29 70510.86 72207.14 67881.00
## [260] 69536.43 62390.71 50113.14 45565.57 45805.29 41348.57 51426.86
## [267] 47160.57 51907.43 49751.43 54407.43 54746.29 61634.57 58926.43
## [274] 69999.29 63044.86 63285.29 61395.43 67969.43 60792.57 56859.14
## [281] 44899.43 43064.14 62790.29 69120.71 69589.43 66633.29 65588.57
## [288] 70168.57 74644.71 52891.00 41560.57 34704.86 46520.00 50231.00
## [295] 49216.71 76914.86 83720.71 84485.00 89765.00 87702.86 82013.86
## [302] 85982.43 57248.43 52968.43 52601.86 45493.29 42298.86 46423.71
## [309] 37898.00 36435.14 30209.57 34541.86 33604.71 37990.71 35683.43
## [316] 65201.86 62730.57 64589.14 73744.86 76477.71 105647.43 103790.29
## [323] 76122.29 74746.14 72865.71 63652.57 60358.29 25957.14 30178.43
## [330] 30681.57 33337.29 32582.71 39184.43 40415.71 34975.43 34076.14
## [337] 34221.14 28862.57 35729.86 36489.29 36785.14 37787.71 39832.14
## [344] 41917.86 41633.57 33557.00 22759.57 28877.86 27574.00 27104.71
## [351] 24376.14 29732.29 34030.00 39139.71 37066.57 38509.29 40957.29
## [358] 49423.00 50053.29 50284.14 53103.86 50223.00 49587.14 41167.71
## [365] 37958.71 33582.29 31039.43 26526.57 34869.43 37487.43 46514.43
## [372] 39613.43 38980.57 37306.14 36771.29 26317.00 31580.71 23626.57
## [379] 33035.71 44864.57 48946.14 46969.57 49249.57 56370.14 67228.71
## [386] 59457.29 53124.71 52814.14 61262.00 61861.14 71784.71 59313.29
## [393] 61107.00 60603.43 60012.57 58280.43 56862.71 41704.43 51533.00
## [400] 50388.71 49205.29 56533.29 47996.14 47207.57 45292.00 40343.43
## [407] 39004.86 36788.43 30027.57 39040.14 42390.14 36291.14 30668.29
## [414] 47693.00 52094.43 56592.57 47971.43 43762.43 42246.71 46352.43
## [421] 33094.86 32784.86 26212.43 32611.57 42144.86 50034.86 46332.00
## [428] 42976.29 39456.29 39328.29 35296.14 30875.43 27709.00 29513.29
## [435] 31630.43 29346.14 34916.86 42020.86 38303.00 37966.43 41408.14
## [442] 38988.14 43555.29 38114.00 27847.86 26517.00 39518.29 39153.71
## [449] 45623.14 40627.43 41027.71 42882.86 47139.43 35547.57 41099.00
## [456] 35859.57 44524.57 48554.29 51554.29 47810.29 50490.00 50720.71
##
## $interrupt_var
## [1] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1
## [38] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
## [75] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
## [112] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
## [149] 1 1 1 1 1 1 1 1 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
## [186] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
## [223] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
## [260] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
## [297] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
## [334] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
## [371] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
## [408] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
## [445] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
## Levels: 0 1 2
##
## $residuals
## 2 3 4 5 6
## 2.073912e+03 4.063951e+03 -5.614423e+02 2.417811e+03 -3.015845e+03
## 7 8 9 10 11
## 5.019487e+02 -5.680546e+03 -1.159813e+03 -3.935223e+03 -3.576527e+02
## 12 13 14 15 16
## -4.887994e+03 -1.521488e+03 -8.123806e+02 4.575225e+02 -3.181561e+03
## 17 18 19 20 21
## -2.979788e+02 -2.061599e+03 6.679589e+03 -1.532261e+03 -1.201245e+03
## 22 23 24 25 26
## 1.488405e+03 -1.194746e+03 2.339603e+02 1.687107e+03 -7.130346e+03
## 27 28 29 30 31
## 9.865625e+02 8.212423e+03 3.516488e+02 -8.109276e+01 -2.463988e+03
## 32 33 34 35 36
## 1.539087e+03 4.519929e+03 1.031888e+03 2.292628e+03 -1.982345e+03
## 37 38 39 40 41
## 4.521176e+03 4.252591e+03 -2.362107e+03 -3.035327e+03 -1.128814e+03
## 42 43 44 45 46
## -1.074744e+04 7.387543e+03 2.572148e+03 1.354715e+03 8.080937e+03
## 47 48 49 50 51
## 5.862236e+02 6.434409e+03 6.568440e+03 -6.075173e+03 -4.907464e+03
## 52 53 54 55 56
## -5.111645e+03 -7.925403e+03 6.208838e+03 -4.067238e+03 -4.847137e+03
## 57 58 59 60 61
## 3.943941e+03 9.271303e+02 -6.792929e+00 1.642356e+02 -4.979000e+03
## 62 63 64 65 66
## 1.818991e+04 3.520141e+03 -3.787007e+03 5.837144e+03 7.209335e+03
## 67 68 69 70 71
## 1.444987e+04 1.386246e+03 -1.349757e+04 -1.427507e+03 4.549810e+03
## 72 73 74 75 76
## -5.027389e+03 -4.468485e+03 -1.051097e+04 2.556530e+03 -5.345479e+03
## 77 78 79 80 81
## 1.163302e+03 -6.789852e+03 6.800059e+02 -2.243851e+03 -2.574549e+03
## 82 83 84 85 86
## -3.801752e+03 -3.859330e+02 2.452002e+03 3.859915e+03 5.249235e+02
## 87 88 89 90 91
## -4.476702e+02 2.330861e+02 4.331457e+03 -1.179409e+03 1.147384e+03
## 92 93 94 95 96
## -2.079088e+03 -1.037439e+03 1.933025e+02 2.863865e+02 -7.476976e+03
## 97 98 99 100 101
## 2.470287e+03 -8.557437e+03 -2.817816e+03 -3.903754e+03 -1.578981e+03
## 102 103 104 105 106
## -1.106729e+03 3.328157e+03 -2.244657e+03 2.701578e+03 -1.090008e+03
## 107 108 109 110 111
## 1.041132e+03 2.639034e+03 -3.134555e+03 -4.675412e+03 -7.630254e+02
## 112 113 114 115 116
## 1.987696e+03 1.174823e+04 -1.308937e+03 2.622261e+03 4.196106e+03
## 117 118 119 120 121
## 3.402580e+03 -1.221785e+03 -4.812411e+03 -3.762487e+03 2.322147e+03
## 122 123 124 125 126
## -1.753440e+03 1.338801e+03 8.843479e+03 7.477391e+02 3.506937e+01
## 127 128 129 130 131
## -2.606196e+03 2.605053e+03 6.982680e+03 8.825106e+02 -8.623114e+03
## 132 133 134 135 136
## 1.723400e+03 4.095604e+03 -3.239398e+03 -1.455245e+03 -8.715516e+02
## 137 138 139 140 141
## -3.887403e+03 1.213982e+03 -4.802931e+02 -2.895865e+03 1.761654e+03
## 142 143 144 145 146
## -1.860148e+03 -7.793077e+03 2.146909e+03 -3.406038e+03 2.200057e+03
## 147 148 149 150 151
## -1.928684e+02 1.081556e+03 -3.185443e+02 1.390874e+03 1.206848e+03
## 152 153 154 155 156
## 3.362273e+03 -4.889886e+03 -1.152122e+03 -3.205247e+03 6.014516e+03
## 157 158 159 160 161
## 9.738788e+03 -3.136674e+03 -4.464299e+03 3.947661e+03 4.825723e+02
## 162 163 164 165 166
## 2.968230e+03 -5.676908e+03 -6.463138e+03 4.493611e+03 1.766809e+04
## 167 168 169 170 171
## 3.712323e+03 -3.404544e+02 -2.370184e+03 -9.921191e+02 3.720600e+03
## 172 173 174 175 176
## -1.299307e+02 -7.966331e+03 3.061571e+03 4.490164e+03 7.462995e+02
## 177 178 179 180 181
## 8.870268e+03 -9.212123e+03 -3.325424e+03 -1.056090e+04 -1.094992e+04
## 182 183 184 185 186
## 1.623187e+03 9.640647e+03 -1.200717e+03 6.163756e+03 6.719562e+03
## 187 188 189 190 191
## 1.325267e+04 8.394185e+03 -4.166754e+03 2.431864e+03 1.032980e+04
## 192 193 194 195 196
## -1.770397e+03 -2.522701e+03 -1.030734e+04 -6.262428e+03 1.405914e+03
## 197 198 199 200 201
## -5.077474e+03 -9.586533e+03 5.688501e+03 -2.838622e+03 -1.460243e+03
## 202 203 204 205 206
## -5.465118e+02 6.746978e+03 1.005123e+04 6.366884e+02 2.985733e+03
## 207 208 209 210 211
## 3.136696e+03 5.801886e+03 1.280407e+04 -5.833364e+03 -1.134622e+04
## 212 213 214 215 216
## -5.572080e+03 -1.042616e+04 -4.802441e+03 1.838799e+03 -1.273450e+04
## 217 218 219 220 221
## 1.678907e+04 8.002380e+03 1.630579e+03 2.677854e+04 1.233335e+04
## 222 223 224 225 226
## 7.046064e+03 1.371000e+04 -4.326581e+03 -2.044494e+03 3.546908e+03
## 227 228 229 230 231
## 1.348827e+02 2.563235e+03 8.833868e+03 5.604581e+03 -2.145097e+03
## 232 233 234 235 236
## -1.996997e+03 9.316475e+03 -1.168617e+04 -7.297804e+03 -8.457835e+03
## 237 238 239 240 241
## -9.919171e+03 3.363429e+03 1.588036e+03 -8.086376e+03 -8.697572e+03
## 242 243 244 245 246
## 9.465461e+03 -7.519900e+03 2.804058e+03 -1.003221e+04 -3.689353e+03
## 247 248 249 250 251
## 1.803819e+03 1.339450e+03 -1.201434e+04 4.058132e+03 2.403975e+03
## 252 253 254 255 256
## 4.505805e+03 2.363549e+03 -9.665998e+02 1.133832e+04 2.094942e+04
## 257 258 259 260 261
## 3.041116e+03 -4.429257e+03 4.032593e+03 -1.792268e+03 3.685435e+03
## 262 263 264 265 266
## -4.922899e+03 -1.088702e+04 -4.586997e+03 -3.293696e+02 -4.997879e+03
## 267 268 269 270 271
## 9.018046e+03 -4.152702e+03 4.363544e+03 -1.986444e+03 4.574445e+03
## 272 273 274 275 276
## 7.995891e+02 7.388485e+03 -1.405662e+03 1.205992e+04 -4.677704e+03
## 277 278 279 280 281
## 1.707167e+03 -3.951163e+02 7.848628e+03 -5.136552e+03 -2.729016e+03
## 282 283 284 285 286
## -1.121343e+04 -2.481956e+03 1.886572e+04 7.767516e+03 2.643111e+03
## 287 288 289 290 291
## -7.271551e+02 8.399699e+02 6.343006e+03 6.772584e+03 -1.893593e+04
## 292 293 294 295 296
## -1.104632e+04 -7.891263e+03 9.981106e+03 3.253079e+03 -1.039985e+03
## 297 298 299 300 301
## 2.755431e+04 9.888042e+03 4.639152e+03 9.243883e+03 2.516705e+03
## 302 303 304 305 306
## -1.350331e+03 7.644639e+03 -2.459571e+04 -3.488375e+03 -7.344054e+01
## 307 308 309 310 311
## -6.858135e+03 -3.771932e+03 3.175297e+03 -8.994850e+03 -2.924987e+03
## 312 313 314 315 316
## -7.858081e+03 1.974680e+03 -2.790165e+03 2.423829e+03 -3.758617e+03
## 317 318 319 320 321
## 2.779837e+04 -7.533208e+02 3.288704e+03 1.080232e+04 5.445830e+03
## 322 323 324 325 326
## 3.220098e+04 4.571540e+03 -2.145562e+04 1.613730e+03 9.491645e+02
## 327 328 329 330 331
## -6.602564e+03 -1.756767e+03 -3.324731e+04 1.368397e+03 -1.858090e+03
## 332 333 334 335 336
## 3.530828e+02 -2.747890e+03 4.520510e+03 -8.101337e+01 -6.609176e+03
## 337 338 339 340 341
## -2.701809e+03 -1.762264e+03 -7.248947e+03 4.352794e+03 -9.552263e+02
## 342 343 344 345 346
## -1.330347e+03 -5.891738e+02 5.694534e+02 8.488548e+02 -1.278221e+03
## 347 348 349 350 351
## -9.103618e+03 -1.276516e+04 2.892975e+03 -3.816568e+03 -3.133858e+03
## 352 353 354 355 356
## -5.447801e+03 2.319115e+03 1.884519e+03 3.197076e+03 -3.390650e+03
## 357 358 359 360 361
## -1.162528e+02 1.057067e+03 7.359901e+03 5.104786e+02 1.844598e+02
## 362 363 364 365 366
## 2.800205e+03 -2.571953e+03 -6.624878e+02 -8.520118e+03 -4.290305e+03
## 367 368 369 370 371
## -5.831487e+03 -4.507641e+03 -6.773809e+03 5.556291e+03 8.031305e+02
## 372 373 374 375 376
## 7.517050e+03 -7.359570e+03 -1.895190e+03 -3.010471e+03 -2.065921e+03
## 377 378 379 380 381
## -1.204764e+04 2.452739e+03 -1.015205e+04 6.284812e+03 9.800414e+03
## 382 383 384 385 386
## 3.430842e+03 -2.151918e+03 1.874441e+03 6.980565e+03 1.154790e+04
## 387 388 389 390 391
## -5.817394e+03 -5.283680e+03 7.621388e-01 8.723018e+03 1.858231e+03
## 392 393 394 395 396
## 1.125244e+04 -9.986755e+03 2.825834e+03 7.374633e+02 5.915263e+02
## 397 398 399 400 401
## -6.185768e+02 -5.058919e+02 -1.441159e+04 8.809778e+03 -1.018340e+03
## 402 403 404 405 406
## -1.190758e+03 7.182836e+03 -7.828811e+03 -1.074565e+03 -2.293411e+03
## 407 408 409 410 411
## -5.549518e+03 -2.515881e+03 -3.549642e+03 -8.352220e+03 6.633768e+03
## 412 413 414 415 416
## 2.020896e+03 -7.037928e+03 -7.272139e+03 1.472054e+04 4.080127e+03
## 417 418 419 420 421
## 4.689478e+03 -7.905906e+03 -4.497872e+03 -2.294811e+03 3.150081e+03
## 422 423 424 425 426
## -1.373501e+04 -2.331555e+03 -8.630089e+03 3.575988e+03 7.455442e+03
## 427 428 429 430 431
## 6.922504e+03 -3.751401e+03 -3.835532e+03 -4.390659e+03 -1.408636e+03
## 432 433 434 435 436
## -5.327687e+03 -6.185884e+03 -5.446481e+03 -8.445628e+02 -3.215595e+02
## 437 438 439 440 441
## -4.476403e+03 3.112545e+03 5.294655e+03 -4.699796e+03 -1.751531e+03
## 442 443 444 445 446
## 1.987554e+03 -3.473302e+03 3.231982e+03 -6.244509e+03 -1.170312e+04
## 447 448 449 450 451
## -3.963533e+03 1.021360e+04 -1.637986e+03 5.153552e+03 -5.558093e+03
## 452 453 454 455 456
## -7.439464e+02 7.575323e+02 3.375030e+03 -1.197763e+04 3.815543e+03
## 457 458 459 460 461
## -6.328736e+03 6.965453e+03 3.339385e+03 2.779013e+03 -3.615575e+03
## 462 463
## 2.372073e+03 2.351812e+02
##
## $fitted.values
## 2 3 4 5 6 7 8 9
## 17195.37 20075.05 24377.59 24092.33 26472.56 23774.77 24499.26 19676.96
## 10 11 12 13 14 15 16 17
## 19410.51 16722.94 17509.28 14201.35 14253.09 14925.33 16641.28 14942.12
## 18 19 20 21 22 23 24 25
## 15988.60 15354.98 22518.26 21591.82 21065.74 22977.32 22295.61 22955.61
## 26 27 28 29 30 31 32 33
## 24822.63 18681.72 20427.58 28354.35 28412.66 28081.85 25684.20 27102.64
## 34 35 36 37 38 39 40 41
## 30989.54 31341.94 32767.20 30249.40 34190.41 37435.11 34457.61 31232.10
## 42 43 44 45 46 47 48 49
## 30066.72 20538.74 28143.28 30607.57 31709.21 38625.35 38114.16 42829.56
## 50 51 52 53 54 55 56 57
## 47114.17 39728.75 34235.22 29201.12 22267.30 28629.10 25170.71 21426.06
## 58 59 60 61 62 63 64 65
## 25884.73 27158.65 27459.05 27875.57 23699.38 40480.00 42345.01 37536.71
## 66 67 68 69 70 71 72 73
## 41791.66 46763.41 57553.33 55544.43 40619.22 38096.62 41148.96 35384.06
## 74 75 76 77 78 79 80 81
## 30784.40 21381.76 24619.77 20498.98 22608.85 17446.14 19484.57 18702.26
## 82 83 84 85 86 87 88 89
## 17718.90 15765.79 17058.14 20707.37 25175.51 26176.67 26201.91 26825.69
## 90 91 92 93 94 95 96 97
## 30997.84 29815.04 30825.80 28868.15 28058.84 28431.18 28842.40 22346.57
## 98 99 100 101 102 103 104 105
## 25396.01 18346.96 17190.04 15208.41 15511.59 16196.70 20720.37 19793.42
## 106 107 108 109 110 111 112 113
## 23344.58 23132.15 24827.39 27736.98 25206.55 21609.45 21888.02 24564.48
## 114 115 116 117 118 119 120 121
## 35552.94 33725.17 35583.61 38616.13 40594.36 38256.41 33018.34 29318.00
## 122 123 124 125 126 127 128 129
## 31424.58 29684.91 30879.95 38566.40 38204.79 37255.62 34083.38 35884.89
## 130 131 132 133 134 135 136 137
## 41344.35 40778.26 31879.60 33158.82 36384.97 32754.67 31123.55 30198.12
## 138 139 140 141 142 143 144 145
## 26715.88 28146.44 27913.44 25573.35 27620.86 26229.93 19759.09 22824.18
## 146 147 148 149 150 151 152 153
## 20626.09 23637.15 24183.30 25791.83 25975.98 27649.01 28964.58 32031.32
## 154 155 156 157 158 159 160 161
## 27449.84 26704.39 24231.77 30193.07 41157.10 39468.30 36803.20 41880.71
## 162 163 164 165 166 167 168 169
## 43305.34 46760.19 42174.42 37428.10 42915.20 59403.25 61640.60 60036.61
## 170 171 172 173 174 175 176 177
## 56826.12 55207.11 57940.50 56953.47 49157.71 52013.41 55798.70 55835.30
## 178 179 180 181 182 183 184 185
## 63045.41 53439.42 50153.33 40857.21 32300.10 35848.35 46067.00 45516.82
## 186 187 188 189 190 191 192 193
## 51537.44 57347.91 68253.82 73596.90 67219.71 67415.35 74566.25 70193.41
## 194 195 196 197 198 199 200 201
## 65665.20 54786.43 48748.51 50189.05 45733.53 37813.07 44311.05 42518.24
## 202 203 204 205 206 207 208 209
## 42152.08 42635.88 49507.34 58497.88 58123.27 59867.73 61542.40 65376.79
## 210 211 212 213 214 215 216 217
## 74951.22 66943.79 54998.22 49545.58 40439.30 37362.34 40511.50 30417.93
## 218 219 220 221 222 223 224 225
## 47584.91 54989.14 55901.32 78926.22 86506.65 88532.71 96210.58 87058.35
## 226 227 228 229 230 231 232 233
## 80988.38 80565.55 77177.34 76329.27 81120.28 82500.10 76872.14 72030.53
## 234 235 236 237 238 239 240 241
## 77748.60 64244.23 56189.98 48048.89 39564.86 43804.54 45981.80 39357.86
## 242 243 244 245 246 247 248 249
## 32965.40 43365.04 37546.37 41526.92 33702.64 32393.75 36090.69 38946.76
## 250 251 252 253 254 255 256 257
## 29671.72 35677.45 39522.19 44776.17 47525.46 47012.25 57430.58 75127.17
## 258 259 260 261 262 263 264 265
## 74940.11 68174.55 69673.27 65850.99 67313.61 61000.16 50152.57 46134.66
## 266 267 268 269 270 271 272 273
## 46346.45 42408.81 51313.27 47543.88 51737.87 49832.98 53946.70 54246.09
## 274 275 276 277 278 279 280 281
## 60332.09 57939.37 67722.56 61578.12 61790.54 60120.80 65929.12 59588.16
## 282 283 284 285 286 287 288 289
## 56112.86 45546.10 43924.57 61353.20 66946.32 67360.44 64748.60 63825.57
## 290 291 292 293 294 295 296 297
## 67872.13 71826.93 52606.89 42596.12 36538.89 46977.92 50256.70 49360.55
## 298 299 300 301 302 303 304 305
## 73832.67 79845.85 80521.12 85186.15 83364.19 78337.79 81844.14 56456.80
## 306 307 308 309 310 311 312 313
## 52675.30 52351.42 46070.79 43248.42 46892.85 39360.13 38067.65 32567.18
## 314 315 316 317 318 319 320 321
## 36394.88 35566.89 39442.05 37403.49 63483.89 61300.44 62942.54 71031.88
## 322 323 324 325 326 327 328 329
## 73446.44 99218.75 97577.91 73132.41 71916.55 70255.14 62115.05 59204.45
## 330 331 332 333 334 335 336 337
## 28810.03 32539.66 32984.20 35330.60 34663.92 40496.73 41584.60 36777.95
## 338 339 340 341 342 343 344 345
## 35983.41 36111.52 31377.06 37444.51 38115.49 38376.89 39262.69 41069.00
## 346 347 348 349 350 351 352 353
## 42911.79 42660.62 35524.73 25984.88 31390.57 30238.57 29823.94 27413.17
## 354 355 356 357 358 359 360 361
## 32145.48 35942.64 40457.22 38625.54 39900.22 42063.10 49542.81 50099.68
## 362 363 364 365 366 367 368 369
## 50303.65 52794.95 50249.63 49687.83 42249.02 39413.77 35547.07 33300.38
## 370 371 372 373 374 375 376 377
## 29313.14 36684.30 38997.38 46973.00 40875.76 40316.61 38837.21 38364.64
## 378 379 380 381 382 383 384 385
## 29127.98 33778.62 26750.90 35064.16 45515.30 49121.49 47375.13 49389.58
## 386 387 388 389 390 391 392 393
## 55680.81 65274.68 58408.39 52813.38 52538.98 60002.91 60532.27 69300.04
## 394 395 396 397 398 399 400 401
## 58281.17 59865.97 59421.05 58899.01 57368.61 56116.01 42723.22 51407.05
## 402 403 404 405 406 407 408 409
## 50396.04 49350.45 55824.95 48282.14 47585.41 45892.95 41520.74 40338.07
## 410 411 412 413 414 415 416 417
## 38379.79 32406.37 40369.25 43329.07 37940.42 32972.46 48014.30 51903.09
## 418 419 420 421 422 423 424 425
## 55877.33 48260.30 44541.53 43202.35 46829.87 35116.41 34842.52 29035.58
## 426 427 428 429 430 431 432 433
## 34689.41 43112.35 50083.40 46811.82 43846.95 40736.92 40623.83 37061.31
## 434 435 436 437 438 439 440 441
## 33155.48 30357.85 31951.99 33822.55 31804.31 36726.20 43002.80 39717.96
## 442 443 444 445 446 447 448 449
## 39420.59 42461.44 40323.30 44358.51 39550.97 30480.53 29304.68 40791.70
## 450 451 452 453 454 455 456 457
## 40469.59 46185.52 41771.66 42125.32 43764.40 47525.20 37283.46 42188.31
## 458 459 460 461 462 463
## 37559.12 45214.90 48775.27 51425.86 48117.93 50485.53
##
## $shapiro.test
## [1] 0
##
## $levenes.test
## [1] 0
##
## $autcorr
## [1] "No autocorrelation evidence"
##
## $post_sums
## [1] "Post-Est Warning"
##
## $adjr_sq
## [1] 0.858
##
## $fstat.bootstrap
##
## ORDINARY NONPARAMETRIC BOOTSTRAP
##
##
## Call:
## boot::boot(data = x, statistic = f.stat, R = Reps, formula = depvar ~
## ., parallel = parr)
##
##
## Bootstrap Statistics :
## original bias std. error
## t1* 4.577743 0.5553095 2.886802
## t2* 1621.515340 28.8859942 240.429393
## WARNING: All values of t3* are NA
##
## $itsa.plot
##
## $booted.ints
## Parameter Lower CI Median F-value Upper CI
## 1 interrupt_var 1.253283 4.683335 10.50597
## 2 lag_depvar 1282.014643 1634.751605 2064.95691Ahora con las tendencias descompuestas
require(zoo)
require(scales)
Gastos_casa %>%
dplyr::mutate(fecha= lubridate::parse_date_time(fecha, c("%d/%m/%Y"),exact=T)) %>%
dplyr::mutate(fecha2=strftime(fecha, format = "%Y-W%V")) %>%
dplyr::mutate(gastador=ifelse(gastador=="Andrés",1,0)) %>%
dplyr::mutate(treat=ifelse(fecha2>"2019-W26",1,0)) %>%
dplyr::mutate(gasto= dplyr::case_when(gasto=="Arreglo"~"Otros",
gasto=="Aspiradora"~"Otros",
gasto=="aspiradora"~"Otros",
gasto=="Chromecast"~"Otros",
gasto=="Cookidoo"~"Comida",
gasto=="Diosi"~"Mascota",
gasto=="donaciones"~"Otros",
gasto=="Easy"~"Otros",
gasto=="Entel"~"Comunicaciones",
gasto=="VTR"~"Comunicaciones",
gasto=="filtro agua"~"Otros",
gasto=="filtro piscina mspa"~"Otros",
gasto=="Granola Wild Foods"~"Otros",
gasto=="Gas"~"Gas o kerosene",
gasto=="Kerosen"~"Gas o kerosene",
gasto=="Matri Andrés Kogan"~"Otros",
gasto=="Muebles ratan"~"Otros",
gasto=="Netflix"~"Comunicaciones",
gasto=="Nexium"~"Farmacia",
gasto=="Pan Pepperino"~"Comida",
gasto=="Parafina"~"Gas o kerosene",
gasto=="Plata basurero"~"Otros",
gasto=="Plata fiestas patrias basureros"~"Otros",
gasto=="Regalo chocolates"~"Otros",
gasto=="Remedio"~"Farmacia",
gasto=="Remedios"~"Farmacia",
gasto=="ropa tami"~"Otros",
gasto=="Sopapo"~"Otros",
gasto=="uber"~"Otros",
gasto=="Tina"~"Otros",
gasto=="Vacuna Influenza"~"Farmacia",
gasto=="Wild Protein"~"Otros",
gasto=="yaz"~"Farmacia",
gasto=="Yaz"~"Farmacia",
T~gasto)) %>%
dplyr::group_by(gastador, fecha,gasto, .drop=F) %>%
#dplyr::mutate(fecha_simp=week(parse_date(fecha))) %>%
# dplyr::mutate(fecha_simp=tsibble::yearweek(fecha)) %>%#después de diosi. Junio 24, 2019
summarise(monto=sum(monto)) %>%
dplyr::mutate(gastador_nombre=plyr::revalue(as.character(gastador), c("0" = "Tami", "1"="Andrés"))) %>%
ggplot2::ggplot(aes(x = fecha, y = monto, color=as.factor(gastador_nombre))) +
#stat_summary(geom = "line", fun.y = median, size = 1, alpha=0.5, aes(color="blue")) +
geom_line(size=1) +
facet_grid(gasto~.)+
#geom_text(aes(x = fech_ing_qrt, y = perc_dup-0.05, label = paste0(n)), vjust = -1,hjust = 0, angle=45, size=3) +
geom_vline(xintercept = as.Date("2019-06-24"),linetype = "dashed") +
labs(y="Gastos (en miles)",x="Semanas y Meses", subtitle="Interlineado, incorporación de la Diosi; Azul= Tami; Rojo= Andrés") +
ggtitle( "Figura 6. Gastos Semanales por Gastador e ítem (media)") +
scale_y_continuous(labels = f <- function(x) paste0(x/1000)) +
scale_color_manual(name = "Gastador", values= c("blue", "red"), labels = c("Tami", "Andrés")) +
scale_x_yearweek(breaks = "1 month", minor_breaks = "1 week", labels=date_format("%m/%y")) +
guides(color = F)+
sjPlot::theme_sjplot2() +
theme(axis.text.x = element_text(vjust = 0.5,angle = 35)) +
theme(
panel.border = element_blank(),
panel.grid.major = element_blank(),
panel.grid.minor = element_blank(),
axis.line = element_line(colour = "black")
)
autoplot(forecast::mstl(Gastos_casa$monto, lambda = "auto",iterate=5000000,start =
lubridate::decimal_date(as.Date("2019-03-03"))))
# scale_x_continuous(breaks = seq(0,400,by=30))
msts <- forecast::msts(Gastos_casa$monto,seasonal.periods = c(7,30.5,365.25),start =
lubridate::decimal_date(as.Date("2019-03-03")))
#tbats <- forecast::tbats(msts,use.trend = FALSE)
#plot(tbats, main="Multiple Season Decomposition")library(forecast)
fit_month <- Gastos_casa %>%
dplyr::mutate(fecha= lubridate::parse_date_time(fecha, c("%d/%m/%Y"),exact=T)) %>%
dplyr::mutate(fecha_month=strftime(fecha, format = "%Y-%m")) %>%
dplyr::mutate(day=as.Date(as.character(lubridate::floor_date(fecha, "day"))))%>%
dplyr::group_by(fecha_month)%>%
summarise(gasto_total=sum(monto))%>%
data.frame()
model_ets<-ets(fit_month$gasto_total, opt.crit = "mse")
#forecast::autoplot(forecast(model_ets, h = 2))
fit_tbats <- forecast::tbats(fit_month$gasto_total)
autplot<-forecast::autoplot(forecast::forecast(fit_tbats, h=2)) +
theme_bw()
autplot
library(bsts)
library(CausalImpact)
ts_week_covid<-
Gastos_casa %>%
dplyr::mutate(fecha= lubridate::parse_date_time(fecha, c("%d/%m/%Y"),exact=T)) %>%
dplyr::mutate(fecha_week=strftime(fecha, format = "%Y-W%V")) %>%
dplyr::mutate(day=as.Date(as.character(lubridate::floor_date(fecha, "day"))))%>%
dplyr::group_by(fecha_week)%>%
summarise(gasto_total=sum(monto,na.rm=T)/1000,min_day=min(day))%>%
dplyr::ungroup() %>%
dplyr::mutate(covid=dplyr::case_when(min_day>=as.Date("2020-03-17")~1,TRUE~0))%>%
dplyr::mutate(covid=as.factor(covid))%>%
data.frame()
ts_week_covid$gasto_total_na<-ts_week_covid$gasto_total
post_resp<-ts_week_covid$gasto_total[which(ts_week_covid$covid==1)]
ts_week_covid$gasto_total_na[which(ts_week_covid$covid==1)]<-NA
ts_week_covid$gasto_total[which(ts_week_covid$covid==0)]## [1] 98.357 4.780 56.784 50.506 64.483 67.248 49.299 35.786 58.503
## [10] 64.083 20.148 73.476 127.004 81.551 69.599 134.446 58.936 26.145
## [19] 129.927 104.989 130.860 81.893 95.697 64.579 303.471 151.106 49.275
## [28] 76.293 33.940 83.071 119.512 20.942 58.055 71.728 44.090 33.740
## [37] 59.264 77.410 60.831 63.376 48.754 235.284 29.604 115.143 72.419
## [46] 5.980 80.063 149.178 69.918 107.601 72.724 63.203 99.681 130.309
## [55] 195.898 112.066# Model 1
ssd <- list()
# Local trend, weekly-seasonal #https://qastack.mx/stats/209426/predictions-from-bsts-model-in-r-are-failing-completely - PUSE UN GENERALIZED LOCAL TREND
ssd <- AddLocalLevel(ssd, ts_week_covid$gasto_total_na) #AddSemilocalLinearTrend #AddLocalLevel
# Add weekly seasonal
ssd <- AddSeasonal(ssd, ts_week_covid$gasto_total_na,nseasons=5, season.duration = 52) #weeks OJO, ESTOS NO SON WEEKS VERDADEROS. PORQUE TENGO MAS DE EUN AÑO
ssd <- AddSeasonal(ssd, ts_week_covid$gasto_total_na, nseasons = 12, season.duration =4) #years
# For example, to add a day-of-week component to data with daily granularity, use model.args = list(nseasons = 7, season.duration = 1). To add a day-of-week component to data with hourly granularity, set model.args = list(nseasons = 7, season.duration = 24).
model1d1 <- bsts(ts_week_covid$gasto_total_na,
state.specification = ssd, #A list with elements created by AddLocalLinearTrend, AddSeasonal, and similar functions for adding components of state. See the help page for state.specification.
family ="student", #A Bayesian Analysis of Time-Series Event Count Data. POISSON NO SE PUEDE OCUPAR
niter = 20000,
#burn = 200, #http://finzi.psych.upenn.edu/library/bsts/html/SuggestBurn.html Suggest the size of an MCMC burn in sample as a proportion of the total run.
seed= 2125)## =-=-=-=-= Iteration 0 Fri Jul 01 14:59:28 2022
## =-=-=-=-=
## =-=-=-=-= Iteration 2000 Fri Jul 01 14:59:34 2022
## =-=-=-=-=
## =-=-=-=-= Iteration 4000 Fri Jul 01 14:59:41 2022
## =-=-=-=-=
## =-=-=-=-= Iteration 6000 Fri Jul 01 14:59:47 2022
## =-=-=-=-=
## =-=-=-=-= Iteration 8000 Fri Jul 01 14:59:53 2022
## =-=-=-=-=
## =-=-=-=-= Iteration 10000 Fri Jul 01 15:00:00 2022
## =-=-=-=-=
## =-=-=-=-= Iteration 12000 Fri Jul 01 15:00:06 2022
## =-=-=-=-=
## =-=-=-=-= Iteration 14000 Fri Jul 01 15:00:13 2022
## =-=-=-=-=
## =-=-=-=-= Iteration 16000 Fri Jul 01 15:00:19 2022
## =-=-=-=-=
## =-=-=-=-= Iteration 18000 Fri Jul 01 15:00:25 2022
## =-=-=-=-=#,
# dynamic.regression=T)
#plot(model1d1, main = "Model 1")
#plot(model1d1, "components")
impact2d1 <- CausalImpact(bsts.model = model1d1,
post.period.response = post_resp)
plot(impact2d1)+
xlab("Date")+
ylab("Monto Semanal (En miles)")
burn1d1 <- SuggestBurn(0.1, model1d1)
#plot(impact3d_ratio_resp)+
#xlab("Date")+
#ylab("Ratio")+
#scale_x_continuous(breaks=(c(seq(1,nrow(data15a64_rn),12),262)),
# labels=as.character(unlist(data15a64_rn[c(seq(1,nrow(data15a64_rn),12),262),"date"])))+
#theme(axis.text.x = element_text(angle = 45, vjust = 0.5, hjust=.5,size=11),
# plot.caption = element_text(hjust = 0, face= "italic",size=9))+
##scale_y_continuous(labels=scales::percent)+
#labs(caption="Note. The first panel shows the data and a counterfactual prediction for the post-treatment period (Blue dashed line);\nThe second panel shows the difference between observed data and counterfactual predictions;\nThe third panel adds up the pointwise contributions from the second panel;\nBlue area= Prediction intervals.")
#summary(impact2d1)
##summary(impact2d1,"report")
#msts(data15a64_rn$hosp_trauma, seasonal.periods=c(7,365.25))corpus <- Corpus(VectorSource(Gastos_casa$obs)) # formato de texto
d <- tm_map(corpus, tolower)
d <- tm_map(d, stripWhitespace)
d <- tm_map(d, removePunctuation)
d <- tm_map(d, removeNumbers)
d <- tm_map(d, removeWords, stopwords("spanish"))
d <- tm_map(d, removeWords, "menos")
tdm <- TermDocumentMatrix(d)
m <- as.matrix(tdm) #lo vuelve una matriz
v <- sort(rowSums(m),decreasing=TRUE) #lo ordena y suma
df <- data.frame(word = names(v),freq=v) # lo nombra y le da formato de data.frame
#findFreqTerms(tdm)
#require(devtools)
#install_github("lchiffon/wordcloud2")
#wordcloud2::wordcloud2(v, size=1.2)
wordcloud(words = df$word, freq = df$freq,
max.words=100, random.order=FALSE, rot.per=0.35,
colors=brewer.pal(8, "Dark2"), main="Figura 7. Nube de Palabras, Observaciones")
fit_month_gasto <- Gastos_casa %>%
dplyr::mutate(fecha= lubridate::parse_date_time(fecha, c("%d/%m/%Y"),exact=T)) %>%
dplyr::mutate(fecha_month=strftime(fecha, format = "%Y-%m")) %>%
dplyr::mutate(day=as.Date(as.character(lubridate::floor_date(fecha, "day"))))%>%
dplyr::mutate(gasto= dplyr::case_when(gasto=="Arreglo"~"Otros",
gasto=="Aspiradora"~"Otros",
gasto=="aspiradora"~"Otros",
gasto=="Chromecast"~"Otros",
gasto=="Cookidoo"~"Comida",
gasto=="Diosi"~"Mascota",
gasto=="donaciones"~"Otros",
gasto=="Easy"~"Otros",
gasto=="Entel"~"Comunicaciones",
gasto=="VTR"~"Comunicaciones",
gasto=="filtro agua"~"Otros",
gasto=="filtro piscina mspa"~"Otros",
gasto=="Granola Wild Foods"~"Otros",
gasto=="Gas"~"Gas o kerosene",
gasto=="Kerosen"~"Gas o kerosene",
gasto=="Matri Andrés Kogan"~"Otros",
gasto=="Muebles ratan"~"Otros",
gasto=="Netflix"~"Comunicaciones",
gasto=="Nexium"~"Farmacia",
gasto=="Pan Pepperino"~"Comida",
gasto=="Parafina"~"Gas o kerosene",
gasto=="Plata basurero"~"Otros",
gasto=="Plata fiestas patrias basureros"~"Otros",
gasto=="Regalo chocolates"~"Otros",
gasto=="Remedio"~"Farmacia",
gasto=="Remedios"~"Farmacia",
gasto=="ropa tami"~"Otros",
gasto=="Sopapo"~"Otros",
gasto=="uber"~"Otros",
gasto=="Tina"~"Otros",
gasto=="Vacuna Influenza"~"Farmacia",
gasto=="Wild Protein"~"Otros",
gasto=="yaz"~"Farmacia",
gasto=="Yaz"~"Farmacia",
T~gasto)) %>%
dplyr::mutate(fecha_month=factor(fecha_month, levels=format(seq(from = as.Date("2019-03-03"), to = as.Date(substr(Sys.time(),1,10)), by = "1 month"),"%Y-%m")))%>%
dplyr::mutate(gasto=factor(gasto, levels=c("Agua", "Comida", "Comunicaciones","Electricidad", "Enceres", "Farmacia", "Gas o kerosene", "Mascota", "Otros")))%>%
dplyr::group_by(fecha_month, gasto, .drop=F)%>%
dplyr::summarise(gasto_total=sum(monto, na.rm = T)/1000)%>%
data.frame()
fit_month_gasto_21<-
fit_month_gasto %>%
#dplyr::filter()
dplyr::filter(grepl("2021|2022",fecha_month)) %>%
#sacar el ultimo mes
dplyr::filter(as.character(format(as.Date(substr(Sys.time(),1,10)),"%Y-%m"))!=fecha_month) %>%
dplyr::group_by(gasto) %>%
summarise(gasto_prom=mean(gasto_total, na.rm=T)) %>%
data.frame()
fit_month_gasto_20<-
fit_month_gasto %>%
#dplyr::filter()
dplyr::filter(grepl("202",fecha_month)) %>%
#sacar el ultimo mes
dplyr::filter(as.character(format(as.Date(substr(Sys.time(),1,10)),"%Y-%m"))!=fecha_month) %>%
dplyr::group_by(gasto) %>%
summarise(gasto_prom=mean(gasto_total, na.rm=T)) %>%
data.frame()
bind_cols(
bind_rows(fit_month_gasto_21,cbind.data.frame(gasto="Total",gasto_prom=sum(fit_month_gasto_21$gasto_prom))),
bind_rows(fit_month_gasto_20,cbind.data.frame(gasto="Total",gasto_prom=sum(fit_month_gasto_20$gasto_prom)))) %>%
dplyr::select(-3) %>%
knitr::kable(format="html", caption="Tabla. Gastos promedio por ítem a contar del...",
col.names= c("Item","Gasto... 2021","Gasto... 2020")) %>%
kableExtra::kable_classic(bootstrap_options = c("striped", "hover"),font_size = 12) %>%
kableExtra::scroll_box(width = "100%", height = "375px")| Item | Gasto… 2021 | Gasto… 2020 |
|---|---|---|
| Agua | 6.342333 | 7.78050 |
| Comida | 308.922111 | 342.52673 |
| Comunicaciones | 29.530278 | 28.66257 |
| Electricidad | 32.052667 | 27.47343 |
| Enceres | 13.505778 | 23.70827 |
| Farmacia | 9.330722 | 11.21313 |
| Gas o kerosene | 25.946889 | 22.47147 |
| Mascota | 39.631167 | 38.62723 |
| Otros | 53.555667 | 39.40570 |
| NA | 23.952750 | 23.95275 |
| Total | 542.770361 | 565.82178 |